Revise acceptance criteria and restrictions documentation to clarify recent updates and specifications. Key changes include enhanced definitions for position accuracy, image processing quality, and operational parameters, as well as updates to camera specifications and validation requirements. This revision aims to improve clarity and ensure alignment with project goals.

_docs/00_research/02_fact_cards.md

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+# 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.