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+# Acceptance Criteria Assessment
+
+Accessed: 2026-05-01. Rerun after user-approved clarifications: 2026-05-01.
+
+## Research Scope
+
+- **Output class**: Technical-component selection support.
+- **Novelty sensitivity**: High for VPR, embedded AI, and autopilot integration; source preference is current papers and official docs.
+- **Boundary**: Fixed-wing UAV, nadir navigation camera, ArduPilot Plane, Jetson Orin Nano Super, offline Azaion Suite Satellite Service cache, eastern/southern Ukraine terrain.
+
+## Acceptance Criteria
+
+| Criterion | Current Values | Researched Values / Evidence | Cost / Timeline Impact | Status |
+|-----------|----------------|------------------------------|------------------------|--------|
+| AC-1.1 / AC-1.2 frame-center accuracy | <=50 m for >=80%, <=20 m for >=50% in normal segments | Plausible only with periodic satellite anchoring plus VO/IMU propagation. Aerial VPR papers show the mechanism is viable but sensitive to weather, scale, repetition, and tile overlap. | High validation cost. | Keep, high-risk |
+| AC-1.3 drift | VO-only <100 m, IMU-fused <50 m between anchors, anchor age reported | Updated AC now requires `last_satellite_anchor_age_ms`, binned validation, and degraded covariance after a solution-defined max anchor age. | Medium. | Updated |
+| AC-1.4 confidence | 95% covariance ellipse + source label | `GPS_INPUT` supports accuracy fields; source labels must be carried in telemetry/FDR because `GPS_INPUT` has no semantic label field. | Medium. | Keep |
+| AC-2.1 registration | VO >95%; satellite anchoring measured separately | Split is correct: VO success is not the same as cross-domain satellite anchor success. | Medium-high. | Updated |
+| AC-2.2 reprojection | <1 px VO, <2.5 px satellite anchor | Reasonable image-space gates, with coordinate error still dependent on calibration, orthorectification, and satellite georegistration. | Medium. | Keep |
+| AC-3.x resilience | Outliers, sharp turns, disconnected segments, blackout | Technically feasible only through mode switching: VO failure triggers VPR/relocalization, blackout triggers IMU-only propagation with honest covariance growth. | High test cost. | Keep |
+| AC-4.1 latency | <400 ms p95, <=10% frame drops, heavy VPR conditional | Aerial VPR survey reports some re-ranking paths too slow for steady-state use; solution must keep global VPR off the per-frame hot path. | High optimization cost. | Updated |
+| AC-4.2 memory | <8 GB shared | Feasible if descriptors are compressed/pruned and indices are memory-mapped or loaded selectively. | Medium-high. | Keep |
+| AC-4.3 MAVLink | v1 GPS_INPUT only via pymavlink | ArduPilot docs require `GPS1_TYPE=14`; MAVLink defines required lat/lon, velocity, fix, and accuracy fields. MAVSDK should remain telemetry-oriented. | Medium. | Keep |
+| AC-5.2 failsafe | >3 s no estimate triggers fallback, Plane SITL verified | Copter docs are reference only. Plane-specific production parameters must be verified in SITL. | Medium. | Updated |
+| AC-7 object localization | Level-flight AI-camera object GPS | Realistic under level-flight clause; maneuvering estimates must publish conservative bound. | Medium. | Keep |
+| AC-8.x satellite cache | 0.3-0.5 m/px, freshness, offline descriptors, VPR chunks | Resolution is feasible through commercial/service imagery. Storage must count descriptors unless separately budgeted. | Medium-high. | Updated |
+| AC-NEW-1 / 2 startup and spoofing | <30 s first fix, <3 s promotion | Feasible only with prebuilt engines, warmed indices, and verified Plane GPS-health triggers. | Medium-high. | Keep with SITL gate |
+| AC-NEW-3 FDR | <=64 GB per flight, no raw frames | Feasible with segment files and rollover. | Medium. | Keep |
+| AC-NEW-4 / 7 safety budgets | False-position and cache-poisoning probabilities | Appropriate safety gates, but require Monte Carlo and representative flight/replay data. | High. | Keep |
+| AC-NEW-5 environment | -20 C to +50 C, 25 W for 8 h | NVIDIA confirms 25 W mode; thermal design must prevent throttling. | Medium-high. | Keep |
+
+## Restrictions Assessment
+
+| Restriction | Current Values | Researched Values / Evidence | Cost / Timeline Impact | Status |
+|-------------|----------------|------------------------------|------------------------|--------|
+| Camera source of truth | `restrictions.md` pins ADTi 20MP ~5472 x 3648 | User confirmed `restrictions.md` is authoritative. Lens/FOV remains a design parameter. | Medium during module selection. | Updated |
+| Fixed nadir camera | No gimbal stabilization | Good for orthorectification; turn/tilt requires attitude compensation and failure detection. | Medium. | Keep |
+| Terrain/weather | Flat steppe/agricultural, seasonal classes included | Repetitive fields and seasonal changes are VPR hazards; validation must include those classes. | High validation cost. | Updated |
+| Satellite Service boundary | Offline consumer of Suite Satellite Service | Strong separation; cache manifest and ingest-voting contract are required. | Medium. | Keep |
+| 10 GB cache | Includes imagery, manifests, overviews, descriptors unless split | Plausible at 0.5 m/px with compression; 0.3 m/px plus descriptors may exceed unless pruned. | Medium. | Updated |
+| Jetson Orin Nano Super | 67 TOPS INT8, 8 GB, 25 W | Official specs support the restriction; thermal throttling remains a risk. | Medium-high. | Keep |
+| Test data gap | Sample imagery lacks IMU/ground truth | Public datasets help prototype, but final acceptance needs synchronized representative data. | High. | Updated |
+
+## Key Findings
+
+1. Use a hybrid estimator: VO/IMU for frame propagation, satellite/VPR anchors for absolute correction, ESKF covariance as the safety gate.
+2. Do not run heavy VPR/re-ranking every frame; invoke it on cold start, VO failure, covariance growth, sharp turns, and disconnected segments.
+3. Avoid GPL libraries in production dependencies unless the project accepts GPL obligations. GPL VIO/SLAM tools should be benchmarks or references, not selected production components.
+4. The cache must be designed as imagery + metadata + descriptor index, not just raster tiles.
+5. ArduPilot Plane SITL and representative camera+IMU data are blocking validation dependencies, but not blockers for solution drafting.
+
+## Sources
+
+See `_docs/00_research/01_source_registry.md` for the detailed source list.
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+# Question Decomposition
+
+## Classification
+
+- **Original question**: Design a GPS-denied onboard localization system for a fixed-wing UAV using a nadir camera, IMU, preloaded satellite imagery, and ArduPilot `GPS_INPUT`.
+- **Active mode**: Mode A Phase 2, initial solution research.
+- **Research output class**: Technical-component selection.
+- **Question type**: Decision support with knowledge organization.
+- **Timeliness sensitivity**: High for VPR, embedded AI inference, and MAVLink/ArduPilot integration; medium for geometry and filtering fundamentals.
+
+## Research Boundary
+
+| Dimension | Boundary |
+|-----------|----------|
+| Population | Fixed-wing UAV missions; not multirotor hover workflows. |
+| Geography | Eastern/southern Ukraine operational areas east/left of the Dnipro River. |
+| Timeframe | Current implementation target with 2024-2026 component evidence where possible. |
+| Level | Onboard real-time production system, not offline post-processing. |
+| Operating context | 8 h flight, 60 km/h, <=1 km AGL, 3 fps nav camera, Jetson Orin Nano Super, GPS denied/spoofed. |
+| Required interfaces | Offline Satellite Service cache in; MAVLink `GPS_INPUT`, QGC telemetry, FDR records, and object-coordinate API out. |
+| Non-functional envelope | <400 ms p95, <8 GB shared memory, 10 GB persistent cache target, 64 GB FDR cap, safety covariance and false-position budgets. |
+
+## Project Constraint Matrix Summary
+
+| Constraint Area | Binding Constraint |
+|-----------------|-------------------|
+| Camera | ADTi 20MP 20L V1, APS-C, ~5472 x 3648, fixed nadir, no gimbal stabilization. |
+| Sensors | FC IMU/attitude/airspeed/altitude available over MAVLink; original still-image sample lacks synchronized IMU, while Derkachi replay data now provides synchronized IMU and `GLOBAL_POSITION_INT` trajectory. |
+| Reference imagery | Offline cache only, 0.5 m/px minimum and 0.3 m/px ideal, freshness gates, no in-flight provider fetch. |
+| Runtime | Jetson Orin Nano Super, CUDA/TensorRT available, 25 W thermal envelope. |
+| Autopilot | ArduPilot only, v1 emits `GPS_INPUT` only; ODOMETRY intentionally disabled. |
+| Storage | No raw frame retention; tiles + FDR only. Descriptor/index storage must be budgeted. |
+| Safety | Reject weak anchors, never under-report covariance, fail/degrade honestly in blackout and spoofing. |
+| Hard disqualifiers | Per-frame heavy VPR without profiling, runtime dependence on external network, stale-tile confident anchors, GPL production dependency unless licensing is accepted. |
+
+## Perspectives
+
+| Perspective | Focus |
+|-------------|-------|
+| Operator / mission user | Does the system keep the UAV navigable and report honest confidence under spoofing/blackout? |
+| Embedded implementer | Can the pipeline fit <400 ms p95 and <8 GB on Jetson with maintainable interfaces? |
+| Safety reviewer | Are false-position and cache-poisoning paths gated before they can steer the FC or poison future caches? |
+| Field practitioner | Will seasonal agricultural repetition, turns, haze/smoke, and stale imagery break the architecture? |
+| Contrarian | Which attractive libraries or SOTA models fail because of licensing, memory, latency, or input mismatch? |
+
+## Sub-Questions And Query Variants
+
+1. What architecture bounds drift while GPS is denied?
+   - fixed-wing UAV GPS-denied satellite image matching visual odometry
+   - visual odometry satellite imagery accumulated error fixed wing UAV
+   - monocular VIO aerial navigation scale ambiguity satellite anchor
+   - GPS spoofed UAV visual inertial navigation covariance failover
+
+2. Which VO/VIO approach fits one nadir camera + IMU?
+   - OpenVINS monocular visual inertial odometry Jetson
+   - ORB-SLAM3 monocular inertial Jetson UAV limitations
+   - VINS-Fusion fixed wing monocular IMU outdoor aerial
+   - homography visual odometry nadir UAV IMU fusion
+
+3. Which satellite retrieval and matching approach fits offline cache + <400 ms?
+   - aerial visual place recognition survey DINOv2 FAISS
+   - DINOv2 VLAD aerial VPR embedded memory
+   - LightGlue SuperPoint DISK ALIKED TensorRT Jetson
+   - cross-view UAV satellite matching failure modes farmland
+
+4. How should the estimator and safety modes work?
+   - ESKF visual inertial GPS denied UAV covariance
+   - GPS_INPUT horiz_accuracy covariance external GPS ArduPilot
+   - visual blackout IMU dead reckoning UAV covariance growth
+   - false position rejection Mahalanobis gate visual localization
+
+5. What cache format and data contract fit the onboard/Satellite Service boundary?
+   - COG PMTiles MBTiles offline raster cache embedded
+   - satellite tile descriptor index storage FAISS PMTiles
+   - cloud optimized geotiff local update limitations
+   - PMTiles read only update PostgreSQL/PostGIS-backed raster cache
+
+6. How should MAVLink output integrate with ArduPilot Plane?
+   - ArduPilot GPS_INPUT GPS1_TYPE 14 Plane SITL
+   - pymavlink gps_input_send external GPS example
+   - MAVSDK GPS_INPUT support raw MAVLink
+   - ArduPilot EKF GPS glitch spoof failsafe Plane parameters
+
+7. What validation datasets and tests are needed?
+   - AerialVL UAV satellite visual localization dataset
+   - VPAir aerial visual place recognition dataset
+   - EuRoC MAV visual inertial odometry dataset
+   - ArduPilot Plane SITL fake GPS spoofing simulation
+
+## Component Option Search Plan
+
+| Component Area | Option Families / Candidates | Evidence Needed |
+|----------------|------------------------------|-----------------|
+| Camera calibration and geometry | OpenCV calibration/homography; custom NumPy geometry; ROS camera pipeline | Official API for intrinsics, distortion, homography, RANSAC; permissive licensing; Jetson compatibility. |
+| VO / VIO propagation | OpenVINS, ORB-SLAM3, VINS-Fusion, custom homography+IMU ESKF | Exact monocular+IMU input fit, output pose/covariance, licensing, runtime, initialization behavior. |
+| VPR global retrieval | DINOv2-VLAD/AnyLoc, MixVPR/SALAD/SelaVPR, classical NetVLAD/BoW | Aerial benchmark evidence, descriptor size, offline index fit, embedded feasibility. |
+| Local cross-domain matching | LightGlue + DISK/ALIKED, SuperPoint+LightGlue, LoFTR/XFeat, SIFT/ORB baseline | Inputs/outputs, match coordinates, license, runtime knobs, TensorRT/Jetson feasibility. |
+| Vector index | FAISS CPU/GPU, PostgreSQL/pgvector metadata-assisted search, Annoy/HNSWLIB | Top-K retrieval, saved index, memory/compression knobs, ARM/Jetson feasibility. |
+| Estimator | Custom ESKF, factor graph, robot_localization | Covariance output, mode labels, Mahalanobis gates, source-specific update control. |
+| Cache/storage | COG, PostgreSQL/PostGIS manifest, PMTiles, MBTiles, raw tile folders | Offline read/update behavior, storage efficiency, metadata/manifest support. |
+| MAVLink integration | pymavlink, MAVSDK, MAVProxy bridge | `GPS_INPUT` support, ArduPilot `GPS1_TYPE=14`, telemetry subscriptions, QGC status. |
+| FDR | PostgreSQL event index, Parquet export, CBOR segment files | Streaming writes, rollover, compact typed records, replayability. |
+
+## Completeness Audit
+
+- **Cost/resources**: covered by Jetson, cache, thermal, and descriptor storage constraints.
+- **Legal/licensing**: covered; GPL VIO/SLAM tools are not selected for production.
+- **Dependencies**: Satellite Service cache contract, ArduPilot Plane SITL, and synchronized validation data are explicit dependencies.
+- **Operating environment**: fixed-wing, altitude, terrain, seasonal/visibility classes, and blackout cases covered.
+- **Failure modes**: VO failure, stale tiles, spoofing, blackout, thermal throttling, false anchors, cache poisoning covered.
+- **Practitioner concerns**: real-time embedded performance and dataset mismatch covered through survey and benchmark sources.
+- **Change over time**: DINOv2/VPR models and Jetson/TensorRT assumptions require version-pinned profiling during implementation.
+
+## Mode B Round 2 Addendum — User-Requested Technology Check
+
+### Research Output Class
+
+Technical-component selection. The addendum verifies two implementation choices before autodev proceeds to planning:
+
+1. Whether OpenVINS should replace the custom OpenCV-based VO/ESKF direction.
+2. Whether DINOv2-VLAD + ALIKED/LightGlue is still the right satellite retrieval and anchor-verification stack.
+
+### Boundary Clarification
+
+"Custom OpenCV" is treated as OpenCV for calibration, undistortion, feature geometry, homography/RANSAC, and MRE measurement, plus a project-owned ESKF/mode machine. It is not treated as a naive OpenCV-only replacement for VIO.
+
+### Additional Query Variants Executed
+
+- OpenVINS GPL-3 license MSCKF visual inertial odometry documentation monocular IMU 2026
+- OpenVINS visual inertial odometry GPS denied UAV MSCKF limitations monocular high altitude nadir camera
+- why not use OpenVINS production GPL ROS dependency visual inertial odometry limitations
+- OpenCV license BSD 3-Clause camera calibration findHomography RANSAC documentation 4.x
+- custom visual odometry OpenCV homography IMU EKF fixed wing UAV satellite imagery GPS denied 2024
+- DINOv2 VLAD AnyLoc visual place recognition aerial satellite retrieval benchmark 2024 2025
+- DINOv2 VLAD limitations visual place recognition storage compute AnyLoc limitations
+- DINOv2 TensorRT Jetson performance issue embedding accuracy visual place recognition
+- ALIKED LightGlue license local feature matching aerial image registration 2024 2025
+- ALIKED LightGlue ONNX TensorRT Jetson performance benchmark local feature matching
+- aerial visual place recognition survey 2024 runtime memory re-ranking SuperGlue LightGlue satellite UAV retrieval
+
+### Addendum Conclusion
+
+OpenVINS is better than a pure custom OpenCV-only VIO implementation, but the production architecture should keep OpenCV as the utility layer and keep the project-owned ESKF/mode machine as the shipped estimator. OpenVINS becomes a mandatory benchmark/reference because it does not own the satellite anchor, spoofing/blackout, source-label, cache-write, and MAVLink semantics required by the acceptance criteria, and GPLv3 remains a production dependency blocker.
+
+DINOv2-VLAD + CPU-first FAISS + ALIKED/LightGlue remains the preferred anchor stack, with two non-negotiable constraints: retrieval is trigger-based rather than per-frame, and TensorRT/ONNX optimizations are accepted only after descriptor-fidelity and Jetson latency tests.
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+# Source Registry
+
+## Source #1
+- **Title**: Visual Odometry in GPS-Denied Zones for Fixed-Wing UAV with Reduced Accumulative Error Based on Satellite Imagery
+- **Link**: https://www.mdpi.com/2076-3417/14/16/7420
+- **Tier**: L1
+- **Publication Date**: 2024
+- **Timeliness Status**: Currently valid
+- **Target Audience**: UAV visual localization researchers/implementers
+- **Research Boundary Match**: Full match
+- **Summary**: Demonstrates fixed-wing high-altitude monocular VO corrected by satellite imagery; highlights scale ambiguity and accumulated drift.
+- **Related Sub-question**: Architecture / drift bounding
+
+## Source #2
+- **Title**: Visual place recognition for aerial imagery: A survey
+- **Link**: https://arxiv.org/abs/2406.00885
+- **Tier**: L1
+- **Publication Date**: 2024
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Aerial VPR researchers/implementers
+- **Research Boundary Match**: Full match
+- **Summary**: Reviews aerial VPR, retrieval/re-ranking, overlap/scale effects, memory/runtime issues, and georeference recall.
+- **Related Sub-question**: VPR / validation
+
+## Source #3
+- **Title**: OpenVINS documentation
+- **Link**: https://docs.openvins.com/
+- **Tier**: L1
+- **Publication Date**: 2023 latest noted release
+- **Timeliness Status**: Needs verification before implementation
+- **Target Audience**: VIO researchers/implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: OpenVINS is an EKF/MSCKF visual-inertial estimator supporting monocular tracking, calibration, evaluation, and covariance-aware estimation; GPL-3 license.
+- **Related Sub-question**: VO/VIO
+
+## Source #4
+- **Title**: ORB-SLAM3 README
+- **Link**: https://raw.githubusercontent.com/UZ-SLAMLab/ORB_SLAM3/master/README.md
+- **Tier**: L1
+- **Publication Date**: 2021 README, still repository source
+- **Timeliness Status**: Needs verification before implementation
+- **Target Audience**: SLAM implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: ORB-SLAM3 supports monocular visual-inertial SLAM and multi-map operation, requires calibration, and is GPLv3.
+- **Related Sub-question**: VO/VIO alternatives
+
+## Source #5
+- **Title**: OpenCV 4.x documentation via Context7
+- **Link**: https://docs.opencv.org/4.x/
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Computer vision implementers
+- **Research Boundary Match**: Full match for utility layer
+- **Summary**: Documents camera calibration, undistortion, and `findHomography` with RANSAC for robust geometry.
+- **Related Sub-question**: Calibration / geometry
+
+## Source #6
+- **Title**: LightGlue README and Context7 docs
+- **Link**: https://raw.githubusercontent.com/cvg/LightGlue/main/README.md
+- **Tier**: L1
+- **Publication Date**: Current repository, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Feature-matching implementers
+- **Research Boundary Match**: Full match for local matching
+- **Summary**: LightGlue accepts local keypoints/descriptors and returns matched coordinates/scores; supports SuperPoint, DISK, ALIKED, SIFT, adaptive pruning, CUDA, and Apache-2 for code/weights while SuperPoint has restrictive licensing.
+- **Related Sub-question**: Local matching
+
+## Source #7
+- **Title**: AnyLoc README
+- **Link**: https://github.com/AnyLoc/AnyLoc
+- **Tier**: L1
+- **Publication Date**: 2023 repository, accessed 2026-05-01
+- **Timeliness Status**: Needs profiling verification
+- **Target Audience**: VPR implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Provides DINOv2 + VLAD API examples and notes substantial storage/compute requirements for full experiments.
+- **Related Sub-question**: VPR descriptors
+
+## Source #8
+- **Title**: DINOv2 repository
+- **Link**: https://github.com/facebookresearch/dinov2
+- **Tier**: L1
+- **Publication Date**: 2023 repository, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Vision model implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Meta's DINOv2 implementation and models, Apache-2.0 / CC-BY-4.0 license notices.
+- **Related Sub-question**: VPR descriptors
+
+## Source #9
+- **Title**: FAISS documentation and Context7 docs
+- **Link**: https://faiss.ai/index.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Vector search implementers
+- **Research Boundary Match**: Full match
+- **Summary**: FAISS supports dense vector search, top-k retrieval, CPU/GPU indexes, product quantization, and save/load APIs; GPU indexes must be converted to CPU before saving.
+- **Related Sub-question**: Descriptor retrieval
+
+## Source #10
+- **Title**: MAVSDK documentation via Context7
+- **Link**: https://github.com/mavlink/mavsdk
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: MAVLink application implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: MAVSDK provides telemetry APIs including raw GPS, GPS info, status text, position/velocity, and odometry subscriptions; `GPS_INPUT` emission should use raw MAVLink/pymavlink for this project.
+- **Related Sub-question**: MAVLink integration
+
+## Source #11
+- **Title**: ArduPilot MAVProxy GPSInput
+- **Link**: https://ardupilot.org/mavproxy/docs/modules/GPSInput.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: ArduPilot integrators
+- **Research Boundary Match**: Full match
+- **Summary**: External GPS input requires `GPS1_TYPE=14` and accepts MAVLink `GPS_INPUT` fields including WGS84 lat/lon, velocity, fix type, and accuracy.
+- **Related Sub-question**: MAVLink output
+
+## Source #12
+- **Title**: MAVLink common message spec: GPS_INPUT
+- **Link**: https://mavlink.io/en/messages/common.html#GPS_INPUT
+- **Tier**: L1
+- **Publication Date**: Current spec, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: MAVLink implementers
+- **Research Boundary Match**: Full match
+- **Summary**: Defines `GPS_INPUT` fields, fix type semantics, `horiz_accuracy`, and ignore flags.
+- **Related Sub-question**: MAVLink output / confidence
+
+## Source #13
+- **Title**: ArduPilot GPS failsafe and glitch protection
+- **Link**: https://ardupilot.org/copter/docs/gps-failsafe-glitch-protection.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Reference only for Plane
+- **Target Audience**: ArduPilot operators
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Documents GPS glitch protection and notes inertial-only position degrades quickly; Copter-specific defaults must not be assumed for Plane.
+- **Related Sub-question**: Failsafe / spoofing
+
+## Source #14
+- **Title**: ArduPilot EKF failsafe
+- **Link**: https://ardupilot.org/copter/docs/ekf-inav-failsafe.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Reference only for Plane
+- **Target Audience**: ArduPilot operators
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Explains EKF variance failsafe behavior and why spoof/glitch tests must be parameterized.
+- **Related Sub-question**: Failsafe / spoofing
+
+## Source #15
+- **Title**: Jetson Orin Nano Super Developer Kit
+- **Link**: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/
+- **Tier**: L1
+- **Publication Date**: Current page, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Embedded AI implementers
+- **Research Boundary Match**: Full match
+- **Summary**: Confirms 67 INT8 TOPS, 8 GB LPDDR5, 102 GB/s, and 7-25 W power range.
+- **Related Sub-question**: Runtime
+
+## Source #16
+- **Title**: NVIDIA JetPack 6.2 Super Mode blog
+- **Link**: https://developer.nvidia.com/blog/nvidia-jetpack-6-2-brings-super-mode-to-nvidia-jetson-orin-nano-and-jetson-orin-nx-modules/
+- **Tier**: L2
+- **Publication Date**: 2024
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Jetson developers
+- **Research Boundary Match**: Full match
+- **Summary**: Explains 25 W and MAXN Super modes and warns thermal design must accommodate the new power modes or throttling occurs.
+- **Related Sub-question**: Runtime / thermal
+
+## Source #17
+- **Title**: PMTiles Concepts
+- **Link**: https://docs.protomaps.com/pmtiles/
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Geospatial storage implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: PMTiles is single-file tiled archive, efficient for reads, but read-only and not update-in-place.
+- **Related Sub-question**: Cache storage
+
+## Source #18
+- **Title**: GDAL COG driver
+- **Link**: https://gdal.org/en/stable/drivers/raster/cog.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Geospatial raster implementers
+- **Research Boundary Match**: Full match
+- **Summary**: Defines COG creation options for tiled, compressed, overview-enabled GeoTIFFs.
+- **Related Sub-question**: Cache storage
+
+## Source #19
+- **Title**: AerialVL dataset
+- **Link**: https://github.com/hmf21/AerialVL
+- **Tier**: L1
+- **Publication Date**: 2024
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Aerial visual localization researchers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Public aerial localization benchmark with UAV sequences, reference maps, and geo-referenced evaluation data.
+- **Related Sub-question**: Validation
+
+## Source #20
+- **Title**: EuRoC MAV Dataset
+- **Link**: http://projects.asl.ethz.ch/datasets/euroc-mav/
+- **Tier**: L1
+- **Publication Date**: 2016
+- **Timeliness Status**: Stable benchmark
+- **Target Audience**: VIO researchers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Stereo camera + IMU + ground truth benchmark useful for VIO sanity tests but not representative of high-altitude nadir fixed-wing imagery.
+- **Related Sub-question**: Validation
+
+## Source #21
+- **Title**: NVIDIA/TensorRT issue: DINOv2 TensorRT performance/precision on Jetson
+- **Link**: https://github.com/NVIDIA/TensorRT/issues/4348
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: Needs verification
+- **Target Audience**: Jetson/TensorRT implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Reports limited mixed-precision gains for DINOv2-S on Jetson/RTX, suggesting DINOv2 optimization is not automatically beneficial.
+- **Related Sub-question**: Mode B performance risk
+
+## Source #22
+- **Title**: NVIDIA Developer Forum: DINOv2 TensorRT model performance issue
+- **Link**: https://forums.developer.nvidia.com/t/dinov2-tensorrt-model-performance-issue/312251
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: Needs verification
+- **Target Audience**: Jetson/TensorRT implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Reports DINOv2 embedding distance changes after TensorRT conversion on Jetson Orin Nano; requires embedding-fidelity validation before relying on TensorRT descriptors.
+- **Related Sub-question**: Mode B performance/quality risk
+
+## Source #23
+- **Title**: LightGlue license issue discussions
+- **Link**: https://github.com/cvg/LightGlue/issues/120
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: Currently relevant
+- **Target Audience**: Feature-matching implementers
+- **Research Boundary Match**: Full match for licensing
+- **Summary**: Community discussion highlights restrictive SuperPoint licensing inside the LightGlue ecosystem and supports avoiding SuperPoint as default production extractor.
+- **Related Sub-question**: Mode B licensing risk
+
+## Source #24
+- **Title**: ArduPilot issue: GPS_INPUT velocity ignore flag pitfall
+- **Link**: https://github.com/ArduPilot/ardupilot/issues/19633
+- **Tier**: L4
+- **Publication Date**: 2021
+- **Timeliness Status**: Needs SITL verification
+- **Target Audience**: ArduPilot integrators
+- **Research Boundary Match**: Full match for GPS_INPUT caution
+- **Summary**: Reports EKF3 may use zero velocity when `GPS_INPUT_IGNORE_FLAG_VEL_HORIZ` is set, so velocity-source parameters must be tested rather than relying only on ignore flags.
+- **Related Sub-question**: Mode B MAVLink pitfall
+
+## Source #25
+- **Title**: FAISS install documentation
+- **Link**: https://github.com/facebookresearch/faiss/blob/main/INSTALL.md
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Vector search implementers
+- **Research Boundary Match**: Full match
+- **Summary**: FAISS CPU conda package supports aarch64, while GPU package availability is x86-64 focused; Jetson design should assume CPU FAISS unless a custom build is proven.
+- **Related Sub-question**: Mode B FAISS deployment
+
+## Source #26
+- **Title**: GNSS-denied geolocalization of UAVs by visual matching of onboard camera images with orthophotos
+- **Link**: https://ar5iv.labs.arxiv.org/html/2103.14381
+- **Tier**: L1
+- **Publication Date**: 2021
+- **Timeliness Status**: Stable mechanism reference
+- **Target Audience**: UAV visual geolocalization researchers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Demonstrates visual matching with orthophotos and Monte Carlo/local planarity ideas; supports using orthorectified reference maps but does not cover all adversarial visual attacks.
+- **Related Sub-question**: Mode B alternative / security limits
+
+## Source #27
+- **Title**: OpenVINS LICENSE
+- **Link**: https://github.com/rpng/open_vins/blob/master/LICENSE
+- **Tier**: L1
+- **Publication Date**: Current repository, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: VIO implementers / product owners
+- **Research Boundary Match**: Full match for licensing
+- **Summary**: OpenVINS is GPLv3-licensed; this is a production dependency constraint, not a technical capability limitation.
+- **Related Sub-question**: Mode B round 2 — OpenVINS vs custom production estimator
+
+## Source #28
+- **Title**: OpenVINS documentation and Context7 lookup
+- **Link**: https://docs.openvins.com/index.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: VIO implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: OpenVINS is a strong EKF/MSCKF VIO system for monocular camera + IMU reference runs, with calibration and covariance-aware state estimation, but it does not own the project-specific satellite anchor, GPS_INPUT, source-label, spoofing, blackout, and cache-poisoning state machine.
+- **Related Sub-question**: Mode B round 2 — OpenVINS vs custom production estimator
+
+## Source #29
+- **Title**: OpenCV 4.x calibration/homography documentation and Context7 lookup
+- **Link**: https://docs.opencv.org/4.x/
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Computer vision implementers
+- **Research Boundary Match**: Full match for geometry utility layer
+- **Summary**: OpenCV 4.x provides calibration, undistortion, homography estimation, RANSAC/USAC robust estimation, and reprojection-error primitives under a permissive license; it is a utility layer rather than a complete GPS-denied estimator.
+- **Related Sub-question**: Mode B round 2 — custom OpenCV boundary
+
+## Source #30
+- **Title**: AnyLoc: Towards Universal Visual Place Recognition
+- **Link**: https://arxiv.org/html/2308.00688
+- **Tier**: L1
+- **Publication Date**: 2023; ICRA 2024
+- **Timeliness Status**: Currently valid, profiling required before deployment
+- **Target Audience**: VPR implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: AnyLoc combines DINOv2 features with VLAD aggregation for broad VPR, including aerial data, and supports the selected DINOv2-VLAD retrieval family while leaving runtime/storage tuning as a deployment gate.
+- **Related Sub-question**: Mode B round 2 — satellite retrieval
+
+## Source #31
+- **Title**: ALIKED-LightGlue-ONNX and LightGlue ONNX/TensorRT deployment reports
+- **Link**: https://github.com/ikeboo/ALIKED-LightGlue-ONNX
+- **Tier**: L2
+- **Publication Date**: Current repository, accessed 2026-05-01
+- **Timeliness Status**: Promising but needs Jetson verification
+- **Target Audience**: Local feature matching implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: ONNX/optimized variants show a credible deployment path for ALIKED + LightGlue, but public evidence is not enough to assume Jetson Orin Nano p95 latency without project profiling.
+- **Related Sub-question**: Mode B round 2 — local matcher deployability
+
+## Source #32
+- **Title**: Visual place recognition for aerial imagery: A survey
+- **Link**: https://arxiv.org/abs/2406.00885
+- **Tier**: L1
+- **Publication Date**: 2024
+- **Timeliness Status**: Currently valid
+- **Target Audience**: Aerial VPR researchers / implementers
+- **Research Boundary Match**: Full match
+- **Summary**: Aerial VPR performance depends materially on tile scale, overlap, weather, repetitive patterns, and re-ranking cost; this supports overlapped VPR chunks, dynamic top-K, and conditional local verification.
+- **Related Sub-question**: Mode B round 2 — satellite retrieval and anchor verification
+
+## Source #33
+- **Title**: BASALT repository and documentation
+- **Link**: https://github.com/VladyslavUsenko/basalt
+- **Tier**: L1
+- **Publication Date**: Current repository, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: VIO implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: BASALT provides visual-inertial odometry and mapping, camera/IMU calibration tools, EuRoC/TUM VI support, and a BSD-style production-friendly licensing path.
+- **Related Sub-question**: Mode B round 3 — Kimera vs BASALT vs OpenVINS
+
+## Source #34
+- **Title**: HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry
+- **Link**: https://arxiv.org/pdf/2106.11857
+- **Tier**: L1
+- **Publication Date**: 2021
+- **Timeliness Status**: Stable benchmark reference
+- **Target Audience**: VIO researchers / embedded implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Reports EuRoC RMS ATE comparisons including BASALT mean about 0.051 m online stereo and Kimera mean about 0.12 m, plus notes that optimization-based methods often lack direct uncertainty quantification compared with filters.
+- **Related Sub-question**: Mode B round 3 — VIO error and confidence comparison
+
+## Source #35
+- **Title**: OpenVINS issue #402 — up-to-date ATE and RTE metrics
+- **Link**: https://github.com/rpng/open_vins/issues/402
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: Community benchmark, verify in our replay harness
+- **Target Audience**: VIO implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Community EuRoC comparison reports BASALT average ATE about 0.072 m with 100% completion, and OpenVINS average ATE about 0.091 m with about 88.55% completion and a divergence on one hard sequence.
+- **Related Sub-question**: Mode B round 3 — BASALT vs OpenVINS error/completion
+
+## Source #36
+- **Title**: Kimera-VIO mono-inertial parameter issues
+- **Link**: https://github.com/MIT-SPARK/Kimera-VIO/issues/254
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: Relevant implementation caveat
+- **Target Audience**: VIO implementers
+- **Research Boundary Match**: Partial overlap
+- **Summary**: Kimera-VIO stereo path remains strong, but mono-inertial configurations had documented poor default performance; parameter changes improved one EuRoC mono setup to less than about +/-0.2 m per axis.
+- **Related Sub-question**: Mode B round 3 — Kimera mono/nadir risk
+
+## Source #37
+- **Title**: RaD-VIO and downward-facing VIO literature
+- **Link**: https://arxiv.org/abs/1810.08704
+- **Tier**: L1
+- **Publication Date**: 2018
+- **Timeliness Status**: Stable mechanism reference
+- **Target Audience**: MAV downward-camera VIO researchers
+- **Research Boundary Match**: Full match for nadir-camera caveat
+- **Summary**: Downward-facing monocular VIO has planar-scene and observability challenges; range/altitude and IMU constraints are important when the camera sees mostly ground plane.
+- **Related Sub-question**: Mode B round 3 — nadir support and limitations
+
+## Source #38
+- **Title**: OpenVINS covariance documentation and StateHelper APIs
+- **Link**: https://docs.openvins.com/dev-index.html
+- **Tier**: L1
+- **Publication Date**: Current docs, accessed 2026-05-01
+- **Timeliness Status**: Currently valid
+- **Target Audience**: VIO implementers
+- **Research Boundary Match**: Full match for covariance/confidence output
+- **Summary**: OpenVINS maintains EKF covariance and exposes full/marginal covariance helpers, making it the strongest reference for covariance consistency even if GPLv3 blocks default production use.
+- **Related Sub-question**: Mode B round 3 — confidence/covariance support
@@ -0,0 +1,348 @@
+# Fact Cards
+
+## Fact #1
+- **Statement**: Fixed-wing high-altitude monocular VO suffers from scale ambiguity and accumulated error; comparing against satellite imagery can reduce accumulated drift.
+- **Source**: Source #1
+- **Phase**: Phase 2
+- **Target Audience**: UAV localization implementers
+- **Confidence**: High
+- **Related Dimension**: Architecture
+- **Fit Impact**: Supports satellite-anchored hybrid estimator.
+
+## Fact #2
+- **Statement**: Aerial VPR is sensitive to weather, season, scale variation, repetitive patterns, and map tile construction; overlap and scale level materially affect retrieval quality.
+- **Source**: Source #2
+- **Confidence**: High
+- **Related Dimension**: VPR
+- **Fit Impact**: Supports AC-8.6 VPR chunks with overlap and seasonal validation.
+
+## Fact #3
+- **Statement**: Heavy VPR re-ranking can be too slow for steady-state embedded use; survey evidence reports some re-ranking around 1 s and SuperGlue much slower on evaluated hardware.
+- **Source**: Source #2
+- **Confidence**: High
+- **Related Dimension**: Runtime
+- **Fit Impact**: Disqualifies per-frame global VPR/re-ranking unless profiled on Jetson.
+
+## Fact #4
+- **Statement**: OpenVINS is an EKF/MSCKF visual-inertial estimator with monocular tracking and calibration support, but its code is GPL-3.
+- **Source**: Source #3
+- **Confidence**: High
+- **Related Dimension**: VO/VIO
+- **Fit Impact**: Reference/benchmark only unless GPL obligations are accepted.
+
+## Fact #5
+- **Statement**: ORB-SLAM3 supports monocular visual-inertial SLAM and multi-map operation, but it is GPLv3 and expects careful calibration and a SLAM-style runtime stack.
+- **Source**: Source #4
+- **Confidence**: High
+- **Related Dimension**: VO/VIO
+- **Fit Impact**: Rejected as production dependency; useful benchmark/reference.
+
+## Fact #6
+- **Statement**: OpenCV provides camera calibration APIs that output camera matrix and distortion coefficients, and homography estimation APIs including RANSAC.
+- **Source**: Source #5
+- **Confidence**: High
+- **Related Dimension**: Calibration / geometry
+- **Fit Impact**: Selected utility layer for calibration, undistortion, homography, and geometric validation.
+
+## Fact #7
+- **Statement**: LightGlue accepts local keypoints/descriptors from extractors such as DISK, ALIKED, SIFT, and SuperPoint, and returns matched keypoint indices, coordinates, and confidence scores.
+- **Source**: Source #6
+- **Confidence**: High
+- **Related Dimension**: Local matching
+- **Fit Impact**: Selected candidate for conditional cross-domain local matching.
+
+## Fact #8
+- **Statement**: LightGlue has adaptive depth/width pruning, FlashAttention, mixed precision, and benchmark scripts; runtime must be profiled on Jetson because defaults are optimized for desktop GPUs.
+- **Source**: Source #6
+- **Confidence**: High
+- **Related Dimension**: Runtime
+- **Fit Impact**: Selected with runtime-quality gate.
+
+## Fact #9
+- **Statement**: LightGlue code/weights are Apache-2.0, but SuperPoint pretrained weights/inference have restrictive licensing; DISK and ALIKED are safer extractor pairings from a licensing perspective.
+- **Source**: Source #6
+- **Confidence**: High
+- **Related Dimension**: Licensing
+- **Fit Impact**: Select DISK/ALIKED+LightGlue for production candidate; treat SuperPoint as license-gated.
+
+## Fact #10
+- **Statement**: AnyLoc provides DINOv2 feature extraction and VLAD aggregation APIs, but its full experiment setup notes large storage/compute requirements.
+- **Source**: Source #7
+- **Confidence**: High
+- **Related Dimension**: VPR descriptors
+- **Fit Impact**: DINOv2-VLAD selected as offline/conditional retrieval candidate, not unconditional per-frame path.
+
+## Fact #11
+- **Statement**: DINOv2 official repository provides Meta's DINOv2 implementation and model assets with Apache-2.0 / CC-BY-4.0 license notices.
+- **Source**: Source #8
+- **Confidence**: High
+- **Related Dimension**: VPR descriptors
+- **Fit Impact**: Supports DINOv2 as a permissible descriptor backbone subject to model-license review.
+
+## Fact #12
+- **Statement**: FAISS is designed for efficient dense vector similarity search, top-k nearest-neighbor retrieval, speed/accuracy tradeoffs, and indexes too large for simple exhaustive scanning.
+- **Source**: Source #9
+- **Confidence**: High
+- **Related Dimension**: Descriptor retrieval
+- **Fit Impact**: Selected vector index for offline VPR descriptors.
+
+## Fact #13
+- **Statement**: FAISS supports saving/loading indexes; GPU indexes must be converted to CPU before saving.
+- **Source**: Source #9
+- **Confidence**: High
+- **Related Dimension**: Cache lifecycle
+- **Fit Impact**: Supports install-time/index-build flow with runtime load.
+
+## Fact #14
+- **Statement**: MAVSDK provides telemetry subscriptions for raw GPS, GPS info, status text, odometry, and position/velocity; it does not remove the need for raw MAVLink control over `GPS_INPUT` emission.
+- **Source**: Source #10
+- **Confidence**: High
+- **Related Dimension**: MAVLink integration
+- **Fit Impact**: Select MAVSDK for telemetry, pymavlink/raw MAVLink for `GPS_INPUT`.
+
+## Fact #15
+- **Statement**: ArduPilot GPSInput requires `GPS1_TYPE=14` for MAVLink GPS input.
+- **Source**: Source #11
+- **Confidence**: High
+- **Related Dimension**: MAVLink output
+- **Fit Impact**: Confirms production parameter requirement.
+
+## Fact #16
+- **Statement**: `GPS_INPUT` carries WGS84 lat/lon, MSL altitude, velocity, `fix_type`, `horiz_accuracy`, `vert_accuracy`, `speed_accuracy`, and ignore flags.
+- **Source**: Source #12
+- **Confidence**: High
+- **Related Dimension**: Output contract
+- **Fit Impact**: Supports mapping estimator covariance to `horiz_accuracy` and failover fix types.
+
+## Fact #17
+- **Statement**: ArduPilot GPS glitch protection and EKF failsafe behavior are parameterized and vehicle-specific; Copter docs are not enough to prove Plane behavior.
+- **Source**: Sources #13, #14
+- **Confidence**: High
+- **Related Dimension**: Failsafe
+- **Fit Impact**: Requires ArduPilot Plane SITL validation.
+
+## Fact #18
+- **Statement**: Jetson Orin Nano Super provides 67 INT8 TOPS, 8 GB memory, 102 GB/s bandwidth, and 7-25 W power range.
+- **Source**: Source #15
+- **Confidence**: High
+- **Related Dimension**: Runtime
+- **Fit Impact**: Confirms target platform constraint.
+
+## Fact #19
+- **Statement**: NVIDIA warns Super power modes require thermal design that can handle the power modes; otherwise throttling can reduce performance.
+- **Source**: Source #16
+- **Confidence**: High
+- **Related Dimension**: Thermal
+- **Fit Impact**: Supports AC-NEW-5 hot-soak and throttle logging.
+
+## Fact #20
+- **Statement**: PMTiles is efficient for single-file tile reads but is read-only and cannot be updated in place.
+- **Source**: Source #17
+- **Confidence**: High
+- **Related Dimension**: Cache storage
+- **Fit Impact**: Rejected for mutable onboard tile writes; possible export/package format only.
+
+## Fact #21
+- **Statement**: COG supports tiled, compressed, overview-enabled GeoTIFFs suitable for efficient raster access and geospatial tooling.
+- **Source**: Source #18
+- **Confidence**: High
+- **Related Dimension**: Cache storage
+- **Fit Impact**: Selected imagery storage unit for immutable service tiles and generated candidate tiles.
+
+## Fact #22
+- **Statement**: AerialVL provides aerial visual localization sequences, reference maps, and geo-referenced evaluation data.
+- **Source**: Source #19
+- **Confidence**: Medium
+- **Related Dimension**: Validation
+- **Fit Impact**: Selected validation dataset for VPR/satellite-anchor algorithm development.
+
+## Fact #23
+- **Statement**: EuRoC provides synchronized camera/IMU and ground truth for VIO, but it is not representative of high-altitude fixed-wing nadir imagery.
+- **Source**: Source #20
+- **Confidence**: High
+- **Related Dimension**: Validation
+- **Fit Impact**: Use for VIO sanity checks only, not final AC proof.
+
+## MVE Evidence
+
+### MVE — OpenCV calibration and homography utilities
+- **Source**: Source #5
+- **Pinned mode/config**: Use OpenCV 4.x C++/Python APIs for checkerboard calibration, undistortion, homography estimation with RANSAC, and reprojection-error measurement.
+- **Inputs in example**: Object/image point correspondences, image size, matched keypoints.
+- **Outputs in example**: Camera matrix, distortion coefficients, rotation/translation vectors, homography matrix.
+- **Project inputs**: ADTi nav-camera frames, checkerboard calibration images, matched VO/satellite points.
+- **Project outputs required**: Intrinsics/distortion, homography, inlier mask, MRE.
+- **Match assessment**: Exact match.
+
+### MVE — LightGlue in DISK/ALIKED local-matching mode
+- **Source**: Source #6
+- **Pinned mode/config**: Use DISK+LightGlue or ALIKED+LightGlue on CUDA/TensorRT-profiled Jetson path, with inputs two normalized images and outputs matched keypoint coordinates plus confidence scores.
+- **Inputs in example**: Two images loaded to GPU; local features extracted by DISK/ALIKED/SuperPoint.
+- **Outputs in example**: `matches` shape `(K, 2)`, keypoint coordinates in each image, confidence scores.
+- **Project inputs**: Orthorectified nav frame crop and candidate satellite/VPR chunk.
+- **Project outputs required**: 2D-2D correspondences for RANSAC homography and cross-domain MRE.
+- **Match assessment**: Exact interface match; runtime quality gate remains.
+
+### MVE — FAISS top-K VPR retrieval
+- **Source**: Source #9
+- **Pinned mode/config**: Use FAISS CPU index with optional GPU acceleration for top-K nearest neighbor search over precomputed DINOv2/VLAD descriptors, saved/loaded at install/preflight time.
+- **Inputs in example**: Float32 descriptor matrix, query descriptor, `k`.
+- **Outputs in example**: Distance matrix `D` and index matrix `I`.
+- **Project inputs**: Precomputed VPR chunk descriptors, query frame descriptor.
+- **Project outputs required**: Top-K candidate chunk IDs for local matching.
+- **Match assessment**: Exact match.
+
+### MVE — MAVSDK telemetry + pymavlink GPS_INPUT
+- **Source**: Sources #10, #11, #12
+- **Pinned mode/config**: Use MAVSDK for telemetry subscriptions and pymavlink/raw MAVLink for `GPS_INPUT` emission to ArduPilot with `GPS1_TYPE=14`.
+- **Inputs in example**: Telemetry streams, estimator lat/lon/alt/velocity/covariance.
+- **Outputs in example**: `GPS_INPUT` fields accepted by ArduPilot GPS backend.
+- **Project inputs**: ESKF state and covariance, source label, mode/fix quality.
+- **Project outputs required**: Frame-by-frame WGS84 `GPS_INPUT`, status text, FDR record.
+- **Match assessment**: Exact match for output contract; Plane SITL validation remains.
+
+## Mode B Findings
+
+### Fact #24
+- **Statement**: DINOv2 TensorRT optimization on Jetson may provide limited speedup and can change embedding distances; descriptor fidelity must be tested against the PyTorch/ONNX baseline before selecting a TensorRT descriptor path.
+- **Source**: Sources #21, #22
+- **Phase**: Mode B
+- **Confidence**: Medium
+- **Related Dimension**: VPR runtime / quality
+- **Fit Impact**: Adds embedding-fidelity gate; keeps DINOv2 selected only after profiling.
+
+### Fact #25
+- **Statement**: LightGlue's SuperPoint path has documented license concerns; DISK/ALIKED remain the safer production default unless legal review approves SuperPoint.
+- **Source**: Source #23
+- **Phase**: Mode B
+- **Confidence**: High
+- **Related Dimension**: Licensing
+- **Fit Impact**: Confirms draft01 decision to avoid SuperPoint as default.
+
+### Fact #26
+- **Statement**: ArduPilot `GPS_INPUT_IGNORE_FLAG_VEL_HORIZ` has a reported EKF3 pitfall where velocity may become zero rather than truly ignored; SITL must validate velocity-source parameters and message fields.
+- **Source**: Source #24
+- **Phase**: Mode B
+- **Confidence**: Medium
+- **Related Dimension**: MAVLink integration
+- **Fit Impact**: Adds a specific MAVLink test and parameter gate.
+
+### Fact #27
+- **Statement**: FAISS deployment on Jetson ARM64 should assume CPU FAISS by default; GPU FAISS packages are not the safe default on aarch64.
+- **Source**: Source #25
+- **Phase**: Mode B
+- **Confidence**: Medium
+- **Related Dimension**: Descriptor retrieval runtime
+- **Fit Impact**: Changes FAISS pinned mode from CPU with optional GPU to CPU-first, with custom GPU build only as future optimization.
+
+### Fact #28
+- **Statement**: Visual matching with orthophotos is a known GNSS-denied UAV approach, but available sources do not prove robustness against adversarial visual attacks on imagery/cache content.
+- **Source**: Source #26
+- **Phase**: Mode B
+- **Confidence**: Medium
+- **Related Dimension**: Security
+- **Fit Impact**: Adds cache integrity, signed manifests, and consistency checks as required controls.
+
+### Fact #29
+- **Statement**: COG creation is a write-new-object workflow; the live onboard cache should append/replace tile objects through manifests, not mutate a COG in place.
+- **Source**: Source #18
+- **Phase**: Mode B
+- **Confidence**: High
+- **Related Dimension**: Cache lifecycle
+- **Fit Impact**: Clarifies cache implementation.
+
+### Fact #30
+- **Statement**: OpenVINS is technically stronger than a pure hand-rolled OpenCV-only VIO stack for camera+IMU odometry, but its GPLv3 license and generic VIO lifecycle make it unsuitable as the default production dependency for this product.
+- **Source**: Sources #27, #28
+- **Phase**: Mode B round 2
+- **Confidence**: High
+- **Related Dimension**: VO / VIO selection
+- **Fit Impact**: Use OpenVINS as a mandatory benchmark/reference, not as the shipped estimator dependency unless GPL obligations are explicitly accepted.
+
+### Fact #31
+- **Statement**: The selected production estimator is not "custom OpenCV-only"; OpenCV is the geometry utility layer, while the product-owned ESKF/mode machine owns covariance, source labels, GPS spoofing, blackout, tile-write eligibility, and MAVLink semantics.
+- **Source**: Sources #5, #29; AC-1.4, AC-3.5, AC-4.3, AC-NEW-4, AC-NEW-7, AC-NEW-8
+- **Phase**: Mode B round 2
+- **Confidence**: High
+- **Related Dimension**: Estimator ownership
+- **Fit Impact**: Keep custom production estimator, but reject any interpretation that means building a naive OpenCV-only VIO stack.
+
+### Fact #32
+- **Statement**: Fixed-wing GPS-denied UAV research supports a hybrid of visual odometry plus satellite/orthophoto matching to reduce accumulated drift, matching the project architecture better than a standalone VIO-only solution.
+- **Source**: Sources #1, #26
+- **Phase**: Mode B round 2
+- **Confidence**: High
+- **Related Dimension**: Architecture
+- **Fit Impact**: Confirms that OpenVINS alone cannot satisfy the absolute-position and re-anchor responsibilities without the satellite anchor path.
+
+### Fact #33
+- **Statement**: DINOv2-VLAD/AnyLoc-style retrieval is a strong global candidate generator for aerial VPR, but descriptor size, model size, and environment-specific VLAD/index choices must be budgeted and profiled.
+- **Source**: Sources #7, #30, #32
+- **Phase**: Mode B round 2
+- **Confidence**: High
+- **Related Dimension**: Satellite retrieval
+- **Fit Impact**: Select DINOv2-VLAD for triggered retrieval, not steady-state per-frame execution.
+
+### Fact #34
+- **Statement**: Aerial VPR sources emphasize tile/chunk scale, overlap, weather/season changes, repetitive patterns, and re-ranking cost; local matching should be a verification/rerank stage over bounded top-K candidates.
+- **Source**: Source #32
+- **Phase**: Mode B round 2
+- **Confidence**: High
+- **Related Dimension**: Anchor verification
+- **Fit Impact**: Supports VPR chunks with 40-50% overlap, dynamic K, and conditional ALIKED/LightGlue verification.
+
+### Fact #35
+- **Statement**: ALIKED + LightGlue has an exact local matching interface and a plausible ONNX/TensorRT deployment path, but public evidence does not prove Jetson Orin Nano p95 latency for the project image sizes.
+- **Source**: Sources #6, #31
+- **Phase**: Mode B round 2
+- **Confidence**: Medium
+- **Related Dimension**: Local matching runtime
+- **Fit Impact**: Keep ALIKED/LightGlue selected with runtime gate; benchmark DISK and SIFT/ORB as fallbacks.
+
+### Fact #36
+- **Statement**: DINOv2 TensorRT conversion can reduce embedding discrimination and may not provide meaningful speedup on Jetson-class devices; descriptor-fidelity tests must precede any optimized engine acceptance.
+- **Source**: Source #22
+- **Phase**: Mode B round 2
+- **Confidence**: Medium
+- **Related Dimension**: VPR deployment
+- **Fit Impact**: TensorRT is an optimization candidate only after PyTorch/ONNX retrieval-rank equivalence is proven.
+
+### Fact #37
+- **Statement**: BASALT is the best production VIO candidate among BASALT, OpenVINS, and Kimera-VIO because it combines permissive licensing with strong published EuRoC accuracy and completion evidence.
+- **Source**: Sources #33, #34, #35
+- **Phase**: Mode B round 3
+- **Confidence**: Medium
+- **Related Dimension**: VO / VIO selection
+- **Fit Impact**: Select BASALT as the production VIO candidate, pending project replay/profiling.
+
+### Fact #38
+- **Statement**: OpenVINS has the clearest EKF covariance story, including full/marginal covariance helpers and NEES-style evaluation support, but remains production-constrained by GPLv3.
+- **Source**: Sources #27, #28, #38
+- **Phase**: Mode B round 3
+- **Confidence**: High
+- **Related Dimension**: Confidence / covariance
+- **Fit Impact**: Keep OpenVINS as covariance/reference baseline and use it to calibrate the BASALT wrapper's reported uncertainty.
+
+### Fact #39
+- **Statement**: Kimera-VIO is production-friendly from a license standpoint, but it is heavier/stereo-oriented and has documented mono-inertial parameter/performance caveats.
+- **Source**: Sources #34, #36
+- **Phase**: Mode B round 3
+- **Confidence**: Medium
+- **Related Dimension**: VO / VIO fallback
+- **Fit Impact**: Keep Kimera-VIO as a backup candidate, not the first production choice for a single fixed nadir camera.
+
+### Fact #40
+- **Statement**: None of BASALT, OpenVINS, or Kimera-VIO provides a special fixed-wing nadir mode; downward-camera support depends on accurate camera-to-IMU extrinsics, altitude/scale constraints, and validation under low-parallax planar terrain.
+- **Source**: Source #37
+- **Phase**: Mode B round 3
+- **Confidence**: High
+- **Related Dimension**: Nadir-camera support
+- **Fit Impact**: The architecture must keep satellite anchors and project-level confidence gates regardless of which VIO library is selected.
+
+### Fact #41
+- **Statement**: Published EuRoC-type VIO error rates are useful for ranking libraries but are not acceptance evidence for high-altitude fixed-wing nadir imagery over agricultural terrain.
+- **Source**: Sources #34, #35, #37
+- **Phase**: Mode B round 3
+- **Confidence**: High
+- **Related Dimension**: Validation
+- **Fit Impact**: Require representative replay/flight data before claiming AC-1/AC-2 accuracy.
@@ -0,0 +1,55 @@
+# Comparison Framework
+
+## Selected Framework Type
+
+Decision support with exact-fit component selection.
+
+## Selected Dimensions
+
+1. Required inputs/outputs and ownership boundaries
+2. Operating context and lifecycle fit
+3. Non-functional envelope fit: latency, memory, storage, thermal, safety
+4. Licensing and deployability
+5. Evidence quality and validation burden
+6. Security and safety failure modes
+7. Selection status
+
+## Initial Population
+
+| Component Area | Candidate | Option Family | Inputs / Outputs | Fit Summary | Status |
+|----------------|-----------|---------------|------------------|-------------|--------|
+| Calibration / geometry | OpenCV 4.x | Established production / open-source | image/object points -> intrinsics, distortion, homography, inlier mask | Exact utility fit; permissive production use. | Selected |
+| VO / VIO | BASALT + project-owned safety/anchor wrapper | Open-source production candidate | nav frames + FC IMU/calibration -> relative VIO state; wrapper adds source labels, anchor fusion, calibrated confidence, and MAVLink semantics | Best production candidate after user decision: permissive license, strong benchmark evidence, and lower implementation burden than custom VIO. | Selected |
+| VO / VIO | OpenVINS | Open-source research | monocular camera + IMU -> VIO state/covariance | Best covariance/reference story, but GPL-3 and generic VIO ownership make it a benchmark/reference rather than shipped core. | Reference only |
+| VO / VIO | Kimera-VIO | Open-source production candidate / fallback | mono or stereo camera + IMU -> VIO/SLAM outputs | BSD-friendly, but heavier/stereo-oriented and mono-inertial path has documented parameter caveats. | Backup candidate |
+| VO / VIO | OpenCV geometry + project-owned ESKF | Custom fallback | nav frames + FC IMU/attitude/altitude + satellite anchors -> relative motion, absolute updates, covariance, source labels | Fallback if BASALT fails project data/runtime tests; still needed as safety/anchor wrapper around any VIO library. | Fallback / wrapper |
+| VO / VIO | ORB-SLAM3 | Open-source research | monocular/stereo/RGB-D + optional IMU -> SLAM pose | Useful benchmark; GPLv3, map runtime, and initialization complexity make it poor production dependency. | Rejected for production |
+| Global retrieval | DINOv2-VLAD / AnyLoc-style descriptors | Current SOTA / research | image/chunk -> descriptor | Strong VPR evidence; trigger-only use due runtime/memory and TensorRT fidelity risk. | Selected with runtime gate |
+| Vector retrieval | FAISS | Established production / open-source | descriptor matrix + query -> top-K IDs | Exact fit for offline VPR chunk retrieval. | Selected |
+| Local matching | LightGlue + DISK/ALIKED | Current SOTA / open-source | two images -> keypoint correspondences/scores | Exact local-match interface; avoids SuperPoint license issue. | Selected with runtime gate |
+| Local matching | SuperPoint + LightGlue | Current SOTA / known-bad/licensing caveat | two images -> matches | Technically good; licensing requires explicit review. | Needs user decision / fallback only |
+| Cache imagery | COG + manifest + sidecars | Established geospatial format | georeferenced raster + metadata -> efficient local reads | Good immutable tile unit; generated tiles can be written as new COGs. | Selected |
+| Cache packaging | PMTiles | Established web-map archive | tile pyramid -> single archive | Efficient reads, but read-only; not suitable for in-flight mutable writes. | Rejected for mutable cache |
+| Estimator | Custom ESKF mode machine | Custom production | VO/IMU/VPR/GPS-health -> WGS84 state + covariance + label | Needed for source labels, covariance gates, blackout/spoofing behavior. | Selected |
+| MAVLink integration | MAVSDK + pymavlink | Established APIs | telemetry in, `GPS_INPUT` out | MAVSDK handles telemetry; pymavlink handles raw `GPS_INPUT`. | Selected |
+| FDR | PostgreSQL event index + CBOR payload segments with optional Parquet export | Established storage | frame estimates, IMU, MAVLink, health, tiles -> bounded replayable log | Matches project PostgreSQL choice while keeping compact append payloads. | Selected pattern |
+| Validation | AerialVL + EuRoC + Plane SITL + representative flight | Multi-source test strategy | datasets/sim/flight -> AC evidence | Public data is partial; final representative data is mandatory. | Selected |
+
+## Round 2 Decision Notes
+
+- **OpenVINS vs custom OpenCV**: OpenVINS wins if the comparison is against a naive OpenCV-only VIO implementation. The selected design is not that; it is OpenCV geometry plus a product-owned estimator/state machine, with OpenVINS used as a benchmark/reference.
+- **Satellite retrieval**: DINOv2-VLAD remains the best global candidate generator found, but aerial VPR sources require chunk scale/overlap tuning, dynamic top-K, and geometric verification.
+- **Anchor verification**: ALIKED/LightGlue remains the preferred learned local matcher, while SIFT/ORB stays as a regression/fallback baseline and SuperPoint remains license-gated.
+
+## Round 3 Decision Notes
+
+- **User decision**: BASALT is selected as the production VIO candidate.
+- **Confidence/covariance**: OpenVINS remains the covariance/reference baseline because its EKF exposes clearer uncertainty semantics than BASALT/Kimera.
+- **Nadir support**: no compared VIO library has a special fixed-wing nadir mode; the acceptance path is calibration, altitude/scale constraints, satellite anchors, and representative replay validation.
+
+## Baseline Alignment
+
+- "Position estimate" means WGS84 frame center emitted to FC plus a 95% covariance semi-major axis and source label.
+- "Satellite anchored" means a visual match passed VPR retrieval, local matching, RANSAC, freshness, covariance, and Mahalanobis gates.
+- "Normal flight segment" means the AC-2.1a conditions, not turns/blackout/stale imagery.
+- "Selected with runtime gate" means the API capability fits, but final deployment depends on Jetson profiling against AC-4.1 and AC-4.2.
@@ -0,0 +1,181 @@
+# Reasoning Chain
+
+## Dimension 1: Core Architecture
+
+### Fact Confirmation
+
+Fixed-wing monocular VO accumulates drift because scale and terrain assumptions are imperfect (Fact #1). Aerial VPR can provide absolute anchors but has weather, scale, and repetition failure modes (Fact #2).
+
+### Reference Comparison
+
+Pure VO/IMU cannot satisfy the long 8-hour mission. Pure satellite matching cannot run every frame inside the latency budget and will fail in turns, stale imagery, and repetitive fields.
+
+### Conclusion
+
+Select a hybrid architecture: steady-state VO/IMU propagation, conditional satellite/VPR anchoring, and ESKF covariance gates.
+
+### Confidence
+
+High. Supported by Sources #1 and #2.
+
+## Dimension 2: VO / VIO Dependency
+
+### Fact Confirmation
+
+OpenVINS and ORB-SLAM3 both support visual-inertial estimation patterns, but both are GPL-family production dependency risks (Facts #4 and #5).
+
+### Reference Comparison
+
+The project needs a production system with custom mode labels, covariance propagation, MAVLink-specific output behavior, and no hidden GPL obligation. A full SLAM stack also adds initialization and map-management complexity that the ACs do not require.
+
+### Conclusion
+
+Use a custom VO/IMU propagation and ESKF mode machine for production. Use OpenVINS/ORB-SLAM3 only as benchmarks/reference algorithms.
+
+### Confidence
+
+High for licensing/scope decision; medium for final estimator performance until prototype profiling.
+
+## Dimension 3: Satellite Retrieval And Local Matching
+
+### Fact Confirmation
+
+Aerial VPR benefits from scale/overlap-aware chunks and retrieval + local alignment (Fact #2). LightGlue provides keypoint correspondences/scores from local descriptors (Fact #7). FAISS provides top-K retrieval over descriptors (Fact #12).
+
+### Reference Comparison
+
+Global DINOv2/VLAD retrieval alone gives candidate chunks but not enough precision for AC-2.2. Local matching alone over the whole map is too expensive. The two-stage retrieval+alignment structure matches the operational need.
+
+### Conclusion
+
+Use DINOv2-VLAD descriptors with FAISS top-K for conditional candidate retrieval, followed by DISK/ALIKED+LightGlue and OpenCV RANSAC homography for local alignment.
+
+### Confidence
+
+Medium-high. API fit is strong; embedded runtime must be profiled.
+
+## Dimension 4: Latency And Memory
+
+### Fact Confirmation
+
+Some aerial VPR re-ranking methods are too slow for the steady-state path (Fact #3). Jetson Orin Nano Super has 8 GB shared memory and 25 W power mode (Fact #18), with thermal throttling risk (Fact #19).
+
+### Reference Comparison
+
+At 3 fps and <400 ms p95, the system can process every frame through lightweight VO/IMU and use heavier VPR only on triggers. Running DINOv2/LightGlue across many candidates per frame would violate the AC unless proven otherwise.
+
+### Conclusion
+
+Make VPR conditional, cap top-K by covariance/sector, run descriptor extraction on downsampled/orthorectified crops, precompute cache descriptors offline, and add performance regression tests.
+
+### Confidence
+
+High for architecture direction; exact model sizes need profiling.
+
+## Dimension 5: Cache Format
+
+### Fact Confirmation
+
+COG supports tiled compressed rasters and geospatial tooling (Fact #21). PMTiles is read-efficient but read-only (Fact #20).
+
+### Reference Comparison
+
+The onboard system must both read service tiles and write new generated tiles in-flight. A read-only archive is a poor primary mutable store.
+
+### Conclusion
+
+Use COG files plus STAC-like manifests/sidecars for imagery and metadata; use FAISS sidecar indexes for descriptors. PMTiles may be an export/snapshot format, not the live mutable cache.
+
+### Confidence
+
+High.
+
+## Dimension 6: Autopilot Integration
+
+### Fact Confirmation
+
+ArduPilot MAVLink GPS input requires `GPS1_TYPE=14` (Fact #15). `GPS_INPUT` carries the fields needed for WGS84 position and accuracy (Fact #16). MAVSDK covers telemetry but raw `GPS_INPUT` emission still needs pymavlink/raw MAVLink (Fact #14).
+
+### Reference Comparison
+
+MAVSDK-only output would not satisfy AC-4.3. Raw pymavlink-only telemetry is possible but gives up MAVSDK's high-level subscriptions.
+
+### Conclusion
+
+Use MAVSDK for telemetry subscriptions and pymavlink for `GPS_INPUT` emission. Verify all failsafe/spoofing behavior in ArduPilot Plane SITL.
+
+### Confidence
+
+High for interface; medium for exact Plane failsafe timing until SITL.
+
+## Dimension 7: Validation
+
+### Fact Confirmation
+
+AerialVL provides aerial localization/reference-map data (Fact #22). EuRoC provides camera+IMU+ground truth but not fixed-wing nadir imagery (Fact #23). The current sample set lacks IMU/ground truth.
+
+### Reference Comparison
+
+No single public dataset proves every AC. Public datasets can de-risk components, while final acceptance requires representative synchronized flight/replay data.
+
+### Conclusion
+
+Use layered validation: EuRoC for VIO sanity, AerialVL/VPAir-style data for VPR/anchor tests, ArduPilot Plane SITL for MAVLink/failsafe/spoofing, and a final representative flight/replay rig for AC proof.
+
+### Confidence
+
+High.
+
+## Dimension 8: OpenVINS vs Project-Owned Estimator
+
+### Fact Confirmation
+
+OpenVINS is a strong monocular+IMU EKF/MSCKF VIO reference with covariance-aware estimation (Facts #4 and #30). OpenCV supplies calibration, undistortion, homography, RANSAC/USAC, and reprojection-error primitives, but it is not a full estimator by itself (Facts #6 and #31).
+
+### Reference Comparison
+
+If the alternative is a naive OpenCV-only VIO stack, OpenVINS is the better technical starting point. The project's actual production choice is different: it needs a project-owned ESKF/mode machine that fuses VO/IMU, accepts/rejects satellite anchors, emits `GPS_INPUT`, labels every estimate, handles spoofing/blackout, and gates cache write-back.
+
+### Conclusion
+
+Keep OpenVINS as a mandatory benchmark/reference implementation, not as the default production dependency. The production estimator remains project-owned, with OpenCV as the geometry utility layer.
+
+### Confidence
+
+High. The technical comparison favors OpenVINS over naive custom VIO, while the product fit favors the project-owned estimator.
+
+## Dimension 9: Satellite Retrieval And Anchor Verification
+
+### Fact Confirmation
+
+DINOv2-VLAD/AnyLoc-style retrieval has strong VPR evidence but descriptor/model size and TensorRT fidelity must be validated (Facts #33 and #36). Aerial VPR survey evidence emphasizes tile scale, overlap, season/weather shifts, repetitive patterns, and re-ranking cost (Fact #34). LightGlue supports ALIKED/DISK/SIFT feature matching and returns correspondences/scores suitable for RANSAC verification, but Jetson latency must be profiled (Facts #7, #8, #35).
+
+### Reference Comparison
+
+Classical SIFT/ORB is simpler and cheap but weaker for cross-domain UAV-to-satellite matching. SuperPoint+LightGlue is technically strong but remains license-gated. Pure global retrieval without local verification is unsafe because repetitive farmland and stale imagery can produce plausible but wrong candidates.
+
+### Conclusion
+
+Use DINOv2-VLAD + CPU-first FAISS as the triggered global retriever, then verify bounded top-K candidates with ALIKED/LightGlue + OpenCV RANSAC. Keep SIFT/ORB as a regression baseline and SuperPoint only after legal approval.
+
+### Confidence
+
+High for architecture and interfaces; medium for final runtime until Jetson profiling.
+
+## Dimension 10: BASALT vs Kimera-VIO vs OpenVINS
+
+### Fact Confirmation
+
+BASALT has strong published EuRoC evidence and production-friendly licensing (Fact #37). OpenVINS has the clearest EKF covariance API and consistency tooling, but GPLv3 remains a production constraint (Fact #38). Kimera-VIO is BSD-friendly but heavier and has documented mono-inertial caveats (Fact #39). All three require calibrated camera-to-IMU extrinsics; none has a special fixed-wing nadir mode (Fact #40).
+
+### Reference Comparison
+
+For a single fixed downward camera, the selection criterion is not just benchmark ATE. The project needs a VIO core that can run on Jetson, tolerate calibrated nadir geometry, and be wrapped by project-specific satellite-anchor, confidence, MAVLink, and safety logic. OpenVINS is attractive for confidence/covariance but problematic as a shipped component. Kimera is acceptable as a BSD fallback, but mono-inertial risk makes it weaker as the first pick. BASALT provides the best production trade-off if its uncertainty can be calibrated and wrapped.
+
+### Conclusion
+
+Select BASALT as the production VIO candidate. Keep OpenVINS as a reference/covariance baseline and Kimera-VIO as a backup candidate. The project-owned safety/anchor wrapper remains mandatory around BASALT because BASALT alone does not satisfy GPS-denied source labels, satellite anchors, false-position budgets, cache-write gates, or `GPS_INPUT` behavior.
+
+### Confidence
+
+Medium-high. Library ranking is well supported; final acceptance still depends on representative fixed-wing nadir replay data.
@@ -0,0 +1,51 @@
+# Validation Log
+
+## Validation Scenario
+
+An 8-hour fixed-wing mission enters GPS-denied/spoofed mode after takeoff. The onboard system starts from last trusted FC state, processes 3 fps nadir frames, emits `GPS_INPUT`, handles normal flight, sharp turns, short visual blackouts, stale/changed tiles, and post-flight tile write-back.
+
+## Expected Based On Conclusions
+
+- **Normal segment**: VO/IMU propagates every processed frame; satellite anchors refresh state conditionally before covariance grows too large.
+- **Sharp turn / <5% overlap**: VO is expected to fail; relocalization uses FAISS top-K VPR chunks followed by LightGlue/RANSAC.
+- **Visual blackout + spoofing**: estimator switches to `dead_reckoned`, covariance grows monotonically, spoofed GPS is ignored, `GPS_INPUT` degrades honestly.
+- **Stale tile**: anchor is rejected or down-confidence weighted and cannot emit `satellite_anchored`.
+- **Cache write-back**: onboard generated tile is written only when parent-pose covariance passes AC-NEW-7 gates and carries metadata for Satellite Service voting.
+
+## Actual Validation Plan
+
+| Validation Target | Test Method | Pass Evidence |
+|-------------------|-------------|---------------|
+| VO/VIO propagation | EuRoC and synthetic nadir replay; then representative flight data | Drift vs anchor-age bins; AC-1.3 pass/fail. |
+| Satellite anchor | AerialVL/VPAir-style benchmark plus project sample imagery with satellite cache | AC-1.1/1.2 accuracy, AC-2.2 MRE, georeference recall. |
+| Runtime | Jetson Orin Nano Super profiling under 25 W, hot-soak included | <400 ms p95, <8 GB memory, no thermal throttle. |
+| VPR retrieval | Offline descriptor build and FAISS query benchmark | Top-K recall, query latency, index size within cache budget. |
+| MAVLink output | ArduPilot Plane SITL with `GPS1_TYPE=14` | Valid `GPS_INPUT`, fix-type/accuracy degradation, QGC status. |
+| Spoofing promotion | Plane SITL false GPS injection | Promotion <3 s and spoofed GPS rejected during blackout. |
+| FDR | 8-hour synthetic load | <=64 GB, rollover logged, no silent payload loss. |
+| Cache poisoning | Monte Carlo with over-confident wrong anchors | AC-NEW-7 probabilities below budget; metadata contract emitted. |
+| OpenVINS reference comparison | Replay the same synchronized camera+IMU segments through OpenVINS and the project-owned estimator | OpenVINS establishes a VIO baseline; production estimator must match/beat drift where applicable while preserving source labels and GPS_INPUT behavior. |
+| BASALT production VIO candidate | Replay synchronized camera+IMU segments through BASALT, OpenVINS, and Kimera-VIO | BASALT selected if drift, completion rate, latency, and wrapper-calibrated covariance meet project gates. |
+| DINOv2-VLAD fidelity | Compare PyTorch, ONNX, and TensorRT descriptor distances and FAISS rankings | Optimized engines accepted only if rank/top-K behavior stays within tolerance. |
+| ALIKED/LightGlue runtime | Jetson benchmark across K candidates and project image sizes | Candidate accepted for runtime only if relocalization trigger path fits AC-4.1 with bounded frame drops. |
+
+## Counterexamples And Risks
+
+- Large DINOv2 variants or many local-match candidates may violate the Jetson latency/memory envelope.
+- Agricultural fields can be visually repetitive; VPR confidence must not be treated as sufficient without geometric verification.
+- Public datasets do not fully match Ukrainian fixed-wing operational conditions; final evidence requires representative data.
+- GPL VIO/SLAM libraries are not production dependencies unless licensing is explicitly accepted.
+- OpenVINS may outperform the first custom estimator prototype on pure VIO drift; that would trigger estimator improvement, not automatic GPL production adoption.
+- BASALT covariance/confidence is less directly exposed than OpenVINS EKF covariance; the project wrapper must calibrate uncertainty before mapping it to `GPS_INPUT.horiz_accuracy`.
+
+## Review Checklist
+
+- [x] Draft conclusions are consistent with fact cards.
+- [x] No important dimensions missed: architecture, VO, VPR, local matching, cache, estimator, MAVLink, FDR, validation covered.
+- [x] No selected component relies only on field-adjacent fit.
+- [x] Mismatches are recorded as rejected/reference/needs-decision rather than hidden.
+- [x] Step 7.5 Component Applicability Gate applies and is saved in `06_component_fit_matrix.md`.
+
+## Conclusions Requiring Revision
+
+Round 3 applies the user decision to select BASALT as the production VIO candidate. The selected implementation is BASALT VIO plus a project-owned safety/anchor wrapper; OpenVINS remains the covariance/reference baseline, Kimera-VIO remains a backup candidate, and custom OpenCV-only VIO is no longer the primary path. Runtime gates and Plane SITL gates are implementation validation gates, not API capability blockers.
@@ -0,0 +1,107 @@
+# Component Fit Matrix
+
+## Top-Level Matrix
+
+| Component Area | Candidate | Pinned Mode/Config | Option Family | Intended Role | API Capability Evidence | Mismatches / Disqualifiers | Status | Decision Rationale |
+|----------------|-----------|--------------------|---------------|---------------|-------------------------|----------------------------|--------|--------------------|
+| Calibration / geometry | OpenCV 4.x | C++/Python calibration, undistortion, RANSAC homography, reprojection-error measurement | Established production / open-source | Camera intrinsics, image normalization, VO/satellite homography verification | MVE: `02_fact_cards.md` OpenCV block; Source #5 | None | Selected | Exact API fit and permissive utility role. |
+| VO / IMU propagation | BASALT + project-owned safety/anchor wrapper | BASALT VIO consumes calibrated nav-camera frames + FC IMU; wrapper fuses satellite anchors, calibrates uncertainty, emits source labels and GPS_INPUT semantics | Open-source production candidate | Relative VIO state, completion/error benchmark, wrapped covariance/confidence, degraded modes, GPS_INPUT semantics | Sources #33-#35; Facts #37, #40, #41 | BASALT covariance/confidence must be calibrated in wrapper; no special nadir mode | Selected | User-selected best production trade-off: permissive licensing and stronger benchmark/completion evidence than OpenVINS/Kimera, with wrapper covering project-specific safety semantics. |
+| VO / VIO reference | OpenVINS | Monocular camera + IMU EKF/MSCKF reference runs with covariance extraction | Open-source research | Benchmark and covariance reference | Sources #3, #27, #28, #35, #38; Facts #4, #30, #38 | GPL-3 production dependency risk; completion/divergence risk on some sequences; does not own satellite anchor / GPS_INPUT / blackout / cache-write state machine | Reference only | Best covariance baseline, but not selected as shipped production dependency. |
+| VO / VIO backup | Kimera-VIO | Mono/stereo camera + IMU VIO/SLAM backup candidate | Open-source production candidate / fallback | Alternative VIO baseline | Sources #34, #36; Fact #39 | Heavier/stereo-oriented; mono-inertial path has documented parameter caveats | Backup candidate | BSD-friendly backup if BASALT fails project replay/runtime gates. |
+| VO / SLAM alternative | ORB-SLAM3 | Monocular-inertial SLAM | Open-source research | Benchmark and failure-mode comparison | Source #4, Fact #5 | GPLv3; heavier SLAM/map lifecycle than required | Rejected | Does not fit licensing/scope for production. |
+| VPR descriptors | DINOv2-VLAD / AnyLoc-style | Precomputed satellite chunk descriptors; conditional query descriptor on relocalization triggers; TensorRT path only after embedding-fidelity check | Current SOTA / research | Global top-K candidate retrieval | Sources #7, #8, #21, #22, #30, #32; Facts #10, #11, #24, #33, #34, #36 | Runtime and embedding-fidelity gates on Jetson; model-size/index-size selection required | Selected with runtime gate | Best evidence for change-robust VPR, but not per-frame and not blindly TensorRT-converted. |
+| Vector retrieval | FAISS | CPU-first aarch64 index; saved/loaded index over float/PQ descriptors; GPU only if custom Jetson build is proven | Established production / open-source | Top-K candidate chunk search | MVE: `02_fact_cards.md` FAISS block; Sources #9, #25 | GPU FAISS not default on Jetson ARM64 | Selected | Exact top-K descriptor retrieval fit with CPU-first deployment. |
+| Local matching | LightGlue + DISK/ALIKED | CUDA/ONNX-profiled DISK or ALIKED feature extraction + LightGlue matching on bounded top-K candidates | Current SOTA / open-source | 2D-2D correspondences for RANSAC and MRE | MVE: `02_fact_cards.md` LightGlue block; Sources #6, #23, #31 | Runtime quality gate; extractor choice must avoid SuperPoint license issue | Selected with runtime gate | Exact input/output fit with deployable licensing path; ALIKED/LightGlue is preferred for anchor verification. |
+| Local matching fallback | SuperPoint + LightGlue | SuperPoint features + LightGlue | Current SOTA / license-gated | Optional benchmark/fallback | Source #6 | SuperPoint restrictive license | Needs user decision | Do not use as default production dependency without legal review. |
+| Cache imagery | COG + manifest/sidecar | Tiled compressed GeoTIFF tile objects with CRS, capture date, source, m/px, freshness, descriptor sidecars; write-new-object lifecycle | Established geospatial format | Immutable service tiles and generated candidate tiles | Source #18, Facts #21, #29 | No in-place mutation; manifest manages active tile version | Selected | Fits geospatial raster access and write-new-tile workflow. |
+| Cache packaging | PMTiles | Read-only tile archive | Established web-map archive | Optional export/snapshot | Source #17, Fact #20 | Cannot update in place | Rejected for live cache | In-flight tile generation needs mutable write-new objects. |
+| MAVLink | MAVSDK + pymavlink | MAVSDK telemetry subscriptions; pymavlink/raw MAVLink `GPS_INPUT` emission to ArduPilot `GPS1_TYPE=14`; velocity source/ignore-flag behavior SITL-tested | Established APIs | FC telemetry, QGC status, GPS substitute output | MVE: `02_fact_cards.md` MAVSDK/pymavlink block; Sources #10-#12, #24 | Plane SITL behavior and velocity-source parameters must be validated | Selected | Exact output contract with known ArduPilot pitfall covered. |
+| Validation | EuRoC + AerialVL/VPAir + Plane SITL + representative flight | Layered validation suite | Test strategy | Prove ACs before production | Sources #19, #20 | Public data not sufficient for final proof | Selected | Covers component de-risking plus final representative proof. |
+
+## Restrictions Cross-Check — Selected Production Architecture
+
+| Restriction | Candidate-mode behavior | Result | Evidence |
+|-------------|-------------------------|--------|----------|
+| Fixed-wing only | Architecture assumes forward motion, rare sharp turns, no hover dependency. | Pass | Problem context; Source #1 |
+| Fixed nadir nav camera | VO/orthorectification uses fixed camera extrinsics; attitude compensation from FC. | Pass | Source #5 |
+| Operational area / flat terrain | Flat terrain assumption supported; repetitive agricultural terrain handled as validation class and confidence risk. | Pass | Source #2 |
+| Weather/season classes | Validation matrix includes seasonal/visibility classes. | Pass | `05_validation_log.md` |
+| Two-camera split | Nav camera drives localization; AI camera object localization uses current GPS-denied state and AI gimbal/zoom. | Pass | AC-7.1/7.2 |
+| Satellite Service offline boundary | Runtime uses local COG/cache + FAISS descriptors only; no in-flight provider fetch. | Pass | Sources #17, #18 |
+| Freshness gates | Tile manifest carries capture date and sector; stale tiles rejected/down-weighted. | Pass | AC-8.2, AC-NEW-6 |
+| Jetson Orin Nano Super | Hot path is lightweight; heavy VPR conditional; runtime profiling gate required. | Pass | Sources #15, #16 |
+| MAVLink / ArduPilot only | MAVSDK telemetry + pymavlink `GPS_INPUT`, `GPS1_TYPE=14`. | Pass | Sources #10-#12 |
+| No raw frame storage | FDR keeps estimates, telemetry, tiles, and low-rate failure thumbnails only. | Pass | AC-8.5, AC-NEW-3 |
+
+## AC Cross-Check — Selected Production Architecture
+
+| AC | Candidate-mode behavior | Result | Evidence |
+|----|-------------------------|--------|----------|
+| AC-1.1 | Satellite anchors and ESKF output target <=50 m for >=80% normal frames. | Pass | Facts #1, #2 |
+| AC-1.2 | Same pipeline targets <=20 m for >=50% normal frames; validated statistically. | Pass | `05_validation_log.md` |
+| AC-1.3 | ESKF tracks anchor age and covariance; VO-only and IMU-fused drift measured between anchors. | Pass | Fact #1 |
+| AC-1.4 | ESKF emits covariance; telemetry/FDR carries source label. | Pass | Fact #16 |
+| AC-2.1a | VO hot path handles normal overlapping frames; failures trigger mode change. | Pass | Facts #1, #6 |
+| AC-2.1b | Satellite anchor success measured separately through VPR + local match + RANSAC. | Pass | Facts #2, #7, #12 |
+| AC-2.2 | OpenCV/LightGlue provide correspondences and homography MRE measurement. | Pass | Facts #6, #7 |
+| AC-3.1 | ESKF innovation gates reject outliers; covariance grows instead of trusting jumps. | Pass | Reasoning chain |
+| AC-3.2 | Sharp turn VO failure triggers VPR relocalization. | Pass | AC design; Source #2 |
+| AC-3.3 | Disconnected segment handled by global retrieval and pose-graph/ESKF re-anchor. | Pass | Source #2 |
+| AC-3.4 | Loss counter and timer trigger GCS relocalization request while dead reckoning continues. | Pass | AC design |
+| AC-3.5 | Image-quality/blackout state switches to IMU-only and rejects spoofed GPS. | Pass | AC design; Facts #16, #17 |
+| AC-4.1 | Heavy VPR is conditional; steady-state path is VO/IMU. Jetson profiling is a runtime quality gate. | Pass | Facts #3, #18 |
+| AC-4.2 | Descriptor/index memory is budgeted; FAISS and cache are precomputed/pruned. | Pass | Facts #12, #13 |
+| AC-4.3 | `GPS_INPUT` emitted by pymavlink; ODOMETRY remains disabled in v1. | Pass | Facts #14-#16 |
+| AC-4.4 | Estimator emits frame-by-frame; no batching required. | Pass | Architecture |
+| AC-4.5 | Corrections are emitted as updated estimates with timestamps and covariance. | Pass | Architecture |
+| AC-5.1 | Startup initializes from last trusted FC state plus IMU propagation. | Pass | Architecture |
+| AC-5.2 | >3 s no-estimate path enters degraded/failsafe behavior; Plane SITL proves FC response. | Pass | Fact #17 |
+| AC-5.3 | Cold restart uses FC state and preloaded cache/index. | Pass | AC-NEW-1 |
+| AC-6.1 | QGC receives downsampled status; FDR keeps high-rate details. | Pass | MAVSDK telemetry + FDR design |
+| AC-6.2 | Command ingress reserved through MAVLink status/named values/custom dialect. | Pass | MAVLink design |
+| AC-6.3 | `GPS_INPUT` lat/lon is WGS84. | Pass | Fact #16 |
+| AC-7.1 | Object localization exposes level-flight accuracy and maneuver bound. | Pass | Geometry design |
+| AC-7.2 | Flat-terrain trig projection from UAV GPS + gimbal/zoom/altitude. | Pass | Geometry design |
+| AC-8.1 | Cache contract requires 0.3-0.5 m/px imagery. | Pass | Restrictions |
+| AC-8.2 | Freshness metadata gates anchors. | Pass | Restrictions |
+| AC-8.3 | Precomputed descriptors and FAISS index are part of cache budget. | Pass | Facts #10, #12, #13 |
+| AC-8.4 | Generated tiles are new COGs with quality metadata for Service ingest. | Pass | Fact #21 |
+| AC-8.5 | Raw frames are not retained. | Pass | FDR design |
+| AC-8.6 | VPR chunks use overlap/multi-scale descriptors and dynamic K. | Pass | Facts #2, #12 |
+| AC-NEW-1 | Engines/indexes built before flight; first fix benchmark validates <30 s. | Pass | Runtime plan |
+| AC-NEW-2 | Plane SITL verifies spoofing trigger and promotion <3 s. | Pass | Fact #17 |
+| AC-NEW-3 | FDR segment rollover validates <=64 GB. | Pass | Validation plan |
+| AC-NEW-4 | Mahalanobis gates and calibrated covariance target false-position budget. | Pass | ESKF design |
+| AC-NEW-5 | Thermal profiling at 25 W validates no throttle. | Pass | Facts #18, #19 |
+| AC-NEW-6 | Tile age rejection/down-weighting built into anchor gate. | Pass | AC design |
+| AC-NEW-7 | Tile writes require parent-pose covariance and sidecar metadata; Satellite Service voting is external contract. | Pass | AC design |
+| AC-NEW-8 | Dead-reckoned blackout mode degrades `GPS_INPUT` fields at covariance thresholds. | Pass | Facts #16, #17 |
+
+## Decision Rules Applied
+
+- No GPL VIO/SLAM library is selected as a production dependency.
+- Runtime gates are classified as runtime-quality validation gates, not API capability blockers.
+- Every selected component has an exact input/output role and a validation path.
+- Any candidate with license uncertainty is marked `Needs user decision` or non-production.
+
+## Mode B Revisions Applied
+
+- FAISS pinned mode changed to CPU-first on Jetson ARM64.
+- DINOv2 TensorRT path requires descriptor-fidelity validation against PyTorch/ONNX.
+- `GPS_INPUT` tests now include velocity-source and ignore-flag behavior.
+- COG cache lifecycle clarified as write-new-object plus manifest versioning, not in-place mutation.
+- Visual/satellite security controls now include signed manifests, cache provenance, stale-tile rejection, and multi-signal consistency checks.
+
+## Mode B Round 2 Revisions Applied
+
+- OpenVINS is explicitly better than naive OpenCV-only VIO, but remains reference-only because the shipped estimator must own source labels, covariance gates, spoofing/blackout states, cache-write eligibility, and MAVLink semantics.
+- The selected VO wording is now "OpenCV geometry + project-owned ESKF" to avoid implying a fragile OpenCV-only odometry implementation.
+- DINOv2-VLAD + CPU-first FAISS + ALIKED/LightGlue remains selected for satellite retrieval and anchor verification, with retrieval limited to relocalization triggers and bounded top-K verification.
+- SIFT/ORB remains a regression/fallback baseline; SuperPoint remains non-production until legal approval.
+
+## Mode B Round 3 Revisions Applied
+
+- BASALT is selected as the production VIO candidate.
+- OpenVINS remains the covariance/reference baseline, not a shipped dependency by default.
+- Kimera-VIO remains a backup VIO candidate because its license is production-friendly but mono-inertial caveats make it weaker for the single-nadir-camera path.
+- The project-owned safety/anchor wrapper remains mandatory around BASALT for satellite anchor acceptance, source labels, blackout/spoofing modes, cache-write eligibility, calibrated confidence, and MAVLink `GPS_INPUT`.