Refactor acceptance criteria, problem description, and restrictions for UAV GPS-Denied system. Enhance clarity and detail in performance metrics, image processing requirements, and operational constraints. Introduce new sections for UAV specifications, camera details, satellite imagery, and onboard hardware.

_docs/00_research/gps_denied_nav/00_ac_assessment.md

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+# Acceptance Criteria Assessment
+
+## Acceptance Criteria
+
+| Criterion | Our Values | Researched Values | Cost/Timeline Impact | Status |
+|-----------|-----------|-------------------|---------------------|--------|
+| Position accuracy (80% of photos) | ≤50m error | 15-150m achievable depending on method. SatLoc (2025): <15m with adaptive fusion. Mateos-Ramirez (2024): 142m mean at 1000m+ altitude. At 400m altitude with better GSD (~6cm/px) and satellite correction, ≤50m for 80% is realistic | Moderate — requires high-quality satellite imagery and robust feature matching pipeline | **Modified** — see notes on satellite imagery quality dependency |
+| Position accuracy (60% of photos) | ≤20m error | Achievable only with satellite-anchored corrections, not with VO alone. SatLoc reports <15m with satellite anchoring + VO fusion. Requires 0.3-0.5 m/px satellite imagery and good terrain texture | High — requires premium satellite imagery, robust cross-view matching, and careful calibration | **Modified** — add dependency on satellite correction frequency |
+| Outlier tolerance | 350m displacement between consecutive photos | At 400m altitude, image footprint is ~375x250m. A 350m displacement means near-zero overlap. VO will fail; system must rely on IMU dead-reckoning or satellite re-localization | Low — standard outlier detection can handle this | Modified — specify fallback strategy (IMU dead-reckoning + satellite re-matching) |
+| Sharp turn handling (partial overlap) | <200m drift, <70° angle, <5% overlap | Standard VO fails below ~20-30% overlap. With <5% overlap, feature matching between consecutive frames is unreliable. Requires satellite-based re-localization or IMU bridging | High — requires separate re-localization module | Modified — clarify: "70%" should likely be "70 degrees"; add IMU-bridge requirement |
+| Disconnected route segments | System should reconnect disconnected chunks | This is essentially a place recognition / re-localization problem. Solvable via satellite image matching for each new segment independently | High — core architectural requirement affecting system design | Modified — add: each segment should independently localize via satellite matching |
+| User fallback input | Ask user after 3 consecutive failures | Reasonable fallback. Needs UI/API integration for interactive input | Low | No change |
+| Processing time per image | <5 seconds | On Jetson Orin Nano Super (8GB shared memory): feasible with optimized pipeline. CUDA feature extraction ~50ms, matching ~100-500ms, satellite crop+match ~1-3s. Full pipeline 2-4s is achievable with image downsampling and TensorRT optimization | Moderate — requires TensorRT optimization and image downsampling strategy | **Modified** — specify this is for Jetson Orin Nano Super, not RTX 2060 |
+| Real-time streaming | SSE for immediate results + refinement | Standard pattern, well-supported | Low | No change |
+| Image Registration Rate | >95% | For consecutive frames with nadir camera in good conditions: 90-98% achievable. Drops significantly during sharp turns and over low-texture terrain (water, uniform fields). The 95% target conflicts with sharp-turn handling requirement | Moderate — requires learning-based matchers (SuperPoint/LightGlue) | **Modified** — clarify: 95% applies to "normal flight" segments only; sharp-turn frames are expected failures handled by re-localization |
+| Mean Reprojection Error | <1.0 pixels | Achievable with modern methods (LightGlue, SuperGlue). Traditional methods typically 1-3 px. Deep learning matchers routinely achieve 0.3-0.8 px with proper calibration | Moderate — requires deep learning feature matchers | No change — achievable |
+| REST API + SSE architecture | Background service | Standard architecture, well-supported in Python (FastAPI + SSE) | Low | No change |
+| Satellite imagery resolution | ≥0.5 m/px, ideally 0.3 m/px | Google Maps for eastern Ukraine: variable, typically 0.5-1.0 m/px in rural areas. 0.3 m/px unlikely from Google Maps. Commercial providers (Maxar, Planet) offer 0.3-0.5 m/px but at significant cost | **High** — Google Maps may not meet 0.5 m/px in all areas of the operational region. 0.3 m/px requires commercial satellite providers | **Modified** — current Google Maps limitation may make this unachievable for all areas; consider fallback for degraded satellite quality |
+| Confidence scoring | Per-position estimate (high=satellite, low=VO) | Standard practice in sensor fusion. Easy to implement | Low | No change |
+| Output format | WGS84, GeoJSON or CSV | Standard, trivial to implement | Negligible | No change |
+| Satellite imagery age | <2 years where possible | Google Maps imagery for conflict zones (eastern Ukraine) may be significantly outdated or intentionally degraded. Recency is hard to guarantee | Medium — may need multiple satellite sources | **Modified** — flag: conflict zone imagery may be intentionally limited |
+| Max VO cumulative drift | <100m between satellite corrections | VIO drift typically 0.8-1% of distance. Between corrections at 1km intervals: ~10m drift. 100m budget allows corrections every ~10km — very generous | Low — easily achievable if corrections happen at reasonable intervals | No change — generous threshold |
+| Memory usage | <8GB shared memory (Jetson Orin Nano Super) | Binding constraint. 8GB LPDDR5 shared between CPU and GPU. ~6-7GB usable after OS. 26MP images need downsampling | **Critical** — all processing must fit within 8GB shared memory | **Updated** — changed to Jetson Orin Nano Super constraint |
+| Object center coordinates | Accuracy consistent with frame-center accuracy | New criterion — derives from problem statement requirement | Low — once frame position is known, object position follows from pixel offset + GSD | **Added** |
+| Sharp turn handling | <200m drift, <70 degrees, <5% overlap. 95% registration rate applies to normal flight only | Clarified from original "70%" to "70 degrees". Split registration rate expectation | Low — clarification only | **Updated** |
+| Offline preprocessing time | Not time-critical (minutes/hours before flight) | New criterion — no constraint existed | Low | **Added** |
+
+## Restrictions Assessment
+
+| Restriction | Our Values | Researched Values | Cost/Timeline Impact | Status |
+|-------------|-----------|-------------------|---------------------|--------|
+| Aircraft type | Fixed-wing only | Appropriate — fixed-wing has predictable motion model, mostly forward flight. Simplifies VO assumptions | N/A | No change |
+| Camera mount | Downward-pointing, fixed, not autostabilized | Implies roll/pitch affect image. At 400m altitude, moderate roll/pitch causes manageable image shift. IMU data can compensate. Non-stabilized means more variable image overlap and orientation | Medium — must use IMU data for image dewarping or accept orientation-dependent accuracy | **Modified** — add: IMU-based image orientation correction should be considered |
+| Operational region | Eastern/southern Ukraine (left of Dnipro) | Conflict zone — satellite imagery may be degraded, outdated, or restricted. Terrain: mix of agricultural, urban, forest. Agricultural areas have seasonal texture changes | **High** — satellite imagery availability and quality is a significant risk | **Modified** — flag operational risk: imagery access in conflict zones |
+| Image resolution | FullHD to 6252x4168, known camera parameters | 26MP at max is large for edge processing. Must downsample for feature extraction. Known camera intrinsics enable proper projective geometry | Medium — pipeline must handle variable resolutions | No change |
+| Altitude | Predefined, ≤1km, terrain height negligible | At 400m: GSD ~6cm/px, footprint ~375x250m. Terrain "negligible" is an approximation — even 50m terrain variation at 400m altitude causes ~12% scale error. The referenced paper (Mateos-Ramirez 2024) shows terrain elevation is a primary error source | **Medium** — "terrain height negligible" needs qualification. At 400m, terrain variations >50m become significant | **Modified** — add: terrain height can be neglected only if variations <50m within image footprint |
+| IMU data availability | "A lot of data from IMU" | IMU provides: accelerometer, gyroscope, magnetometer. Crucial for: dead-reckoning during feature-less frames, image orientation compensation, scale estimation, motion prediction. Standard tactical IMUs provide 100-400Hz data | Low — standard IMU integration | **Modified** — specify: IMU data includes gyroscope + accelerometer at ≥100Hz; will be used for orientation compensation and dead-reckoning fallback |
+| Weather | Mostly sunny | Favorable for visual methods. Shadows can actually help feature matching. Reduces image quality variability | Low — favorable condition | No change |
+| Satellite provider | Google Maps (potentially outdated) | **Critical limitation**: Google Maps satellite API has usage limits, unknown update frequency for eastern Ukraine, potential conflict-zone restrictions. Resolution may not meet 0.5 m/px in rural areas. No guarantee of recency | **High** — single-provider dependency is a significant risk | **Modified** — consider: (1) downloading tiles ahead of time for the operational area, (2) having a fallback provider strategy |
+| Photo count | Up to 3000, typically 500-1500 | At 3fps and 500-1500 photos: 3-8 minutes of flight. At ~100m spacing: 50-150km route. Memory for 3000 pre-extracted satellite feature maps needs careful management on 8GB | Medium — batch processing and memory management needed | **Modified** — add: pipeline must manage memory for up to 3000 frames on 8GB device |
+| Sharp turns | Next photo may have no common objects with previous | This is the hardest edge case. Complete visual discontinuity requires satellite-based re-localization. IMU provides heading/velocity for bridging. System must be architected around this possibility | High — drives core architecture decision | No change — already captured as a defining constraint |
+| Processing hardware | Jetson Orin Nano Super, 67 TOPS | 8GB shared LPDDR5, 1024 CUDA cores, 32 Tensor Cores, 102 GB/s bandwidth. TensorRT for inference optimization. Power: 7-25W. Significantly less capable than desktop GPU | **Critical** — all processing must fit within 8GB shared memory, pipeline must be optimized for TensorRT | **Modified** — CONTRADICTS AC's RTX 2060 reference. Must be the binding constraint |
+
+## Key Findings
+
+1. **CRITICAL CONTRADICTION**: The AC mentions "RTX 2060 compatibility" (16GB RAM + 6GB VRAM) but the restriction specifies Jetson Orin Nano Super (8GB shared memory). These are fundamentally different platforms. **The Jetson must be the binding constraint.** All processing, including model weights, image buffers, and intermediate results, must fit within ~6-7GB usable memory (OS takes ~1-1.5GB).
+
+2. **Satellite Imagery Risk**: Google Maps as the sole satellite provider for a conflict zone in eastern Ukraine presents significant quality, resolution, and recency risks. The 0.3 m/px "ideal" resolution is unlikely available from Google Maps for this region. The system design must be robust to degraded satellite reference quality (0.5-1.0 m/px).
+
+3. **Accuracy is Achievable but Conditional**: The 50m/80% and 20m/60% accuracy targets are achievable based on recent research (SatLoc 2025: <15m with adaptive fusion), but **only when satellite corrections are successful**. VO-only segments will drift ~1% of distance traveled. The system must maximize satellite correction frequency.
+
+4. **Sharp Turn Handling Drives Architecture**: The requirement to handle disconnected route segments with no visual overlap between consecutive frames means the system cannot rely solely on sequential VO. It must have an independent satellite-based geo-localization capability for each frame or segment — this is a core architectural requirement.
+
+5. **Processing Time is Feasible**: <5s per image on Jetson Orin Nano Super is achievable with: (a) image downsampling (e.g., to 2000x1300), (b) TensorRT-optimized models, (c) efficient satellite region cropping. GPU-accelerated feature extraction takes ~50ms, matching ~100-500ms, satellite matching ~1-3s.
+
+6. **Missing AC: Object Center Coordinates**: The problem statement mentions "coordinates of the center of any object in these photos" but no acceptance criterion specifies the accuracy requirement for this. Need to add.
+
+7. **Missing AC: DEM/Elevation Data**: Research shows terrain elevation is a primary error source for pixel-to-meter conversion at these altitudes. If terrain variations are >50m, a DEM is needed. No current restriction mentions DEM availability.
+
+8. **Missing AC: Offline Preprocessing Time**: No constraint on how long satellite image preprocessing can take before the flight.
+
+9. **"70%" in Sharp Turn AC is Ambiguous**: "at an angle of less than 70%" — this likely means 70 degrees, not 70%.
+
+## Sources
+
+- SatLoc: Hierarchical Adaptive Fusion Framework for GNSS-denied UAV Localization (2025) — <15m error, >90% coverage, 2+ Hz on edge hardware
+- Mateos-Ramirez et al. "Visual Odometry in GPS-Denied Zones for Fixed-Wing UAV" (2024) — 142.88m mean error over 17km at 1000m+ altitude, 0.83% error rate with satellite correction
+- NVIDIA Jetson Orin Nano Super specs: 8GB LPDDR5, 67 TOPS, 1024 CUDA cores, 102 GB/s bandwidth
+- cuda-efficient-features: Feature extraction benchmarks — 4K in ~12ms on Jetson Xavier
+- SIFT+LightGlue for UAV image mosaicking (ISPRS 2025) — superior performance across diverse scenarios
+- SuperPoint+LightGlue comparative analysis (2024) — best balance of robustness, accuracy, efficiency
+- Google Maps satellite resolution: 0.15m-30m depending on location and source imagery
+- VIO drift benchmarks: 0.82-1% of distance traveled (EuRoC, outdoor flights)
+- UAVSAR cross-modality matching: 1.83-2.86m RMSE with deep learning approach (Springer 2026)

_docs/00_research/gps_denied_nav/00_question_decomposition.md

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+# Question Decomposition
+
+## Original Question
+Research the GPS-denied onboard navigation problem for a fixed-wing UAV and find the best solution architecture. The system must determine frame-center GPS coordinates using visual odometry, satellite image matching, and IMU fusion — all running on a Jetson Orin Nano Super (8GB shared memory, 67 TOPS).
+
+## Active Mode
+Mode A Phase 2 — Initial Research (Problem & Solution)
+
+## Rationale
+No existing solution drafts. Full problem decomposition and solution research needed.
+
+## Problem Context Summary (from INPUT_DIR)
+- **Platform**: Fixed-wing UAV, camera pointing down (not stabilized), 400m altitude max 1km
+- **Camera**: ADTi Surveyor Lite 26S v2, 26MP (6252x4168), focal length 25mm, sensor width 23.5mm
+- **GSD at 400m**: ~6cm/pixel, footprint ~375x250m
+- **Frame rate**: 3 fps (interval ~333ms, real-world could be 400-500ms)
+- **Photo count**: 500-3000 per flight
+- **IMU**: Available at high rate
+- **Initial GPS**: Known; GPS may be denied/spoofed during flight
+- **Satellite reference**: Pre-uploaded Google Maps tiles
+- **Hardware**: Jetson Orin Nano Super, 8GB shared memory, 67 TOPS
+- **Region**: Eastern/southern Ukraine (conflict zone)
+- **Key challenge**: Reconnecting disconnected route segments after sharp turns
+
+## Question Type Classification
+**Decision Support** — we need to evaluate and select the best architectural approach and component technologies for each part of the pipeline.
+
+## Research Subject Boundary Definition
+
+| Dimension | Boundary |
+|-----------|----------|
+| Population | Fixed-wing UAVs with nadir cameras at 200-1000m altitude |
+| Geography | Rural/semi-urban terrain in eastern Ukraine |
+| Timeframe | Current state-of-the-art (2023-2026) |
+| Level | Edge computing (Jetson-class, 8GB memory), real-time processing |
+
+## Decomposed Sub-Questions
+
+### A. Existing/Competitor Solutions
+1. What existing systems solve GPS-denied UAV visual navigation?
+2. What open-source implementations exist for VO + satellite matching?
+3. What commercial/military solutions address this problem?
+
+### B. Architecture Components
+4. What is the optimal pipeline architecture (sequential vs parallel, streaming)?
+5. How should VO, satellite matching, and IMU fusion be combined (loosely vs tightly coupled)?
+6. How to handle disconnected route segments (the core architectural challenge)?
+
+### C. Visual Odometry Component
+7. What VO algorithms work best for aerial nadir imagery on edge hardware?
+8. What feature extractors/matchers are optimal for Jetson (SuperPoint, ORB, XFeat)?
+9. How to handle scale estimation with known altitude and camera parameters?
+10. What is the optimal image downsampling strategy for 26MP on 8GB memory?
+
+### D. Satellite Image Matching Component
+11. How to efficiently match UAV frames against pre-loaded satellite tiles?
+12. What cross-view matching methods work for aerial-to-satellite registration?
+13. How to preprocess and index satellite tiles for fast retrieval?
+14. How to handle resolution mismatch (6cm UAV vs 50cm+ satellite)?
+
+### E. IMU Fusion Component
+15. How to fuse IMU data with visual estimates (EKF, UKF, factor graph)?
+16. How to use IMU for dead-reckoning during feature-less frames?
+17. How to use IMU for image orientation compensation (non-stabilized camera)?
+
+### F. Edge Optimization
+18. How to fit the full pipeline in 8GB shared memory?
+19. What TensorRT optimizations are available for feature extractors?
+20. How to achieve <5s per frame on Jetson Orin Nano Super?
+
+### G. API & Streaming
+21. What is the best approach for REST API + SSE on Python/Jetson?
+22. How to implement progressive result refinement?
+
+## Timeliness Sensitivity Assessment
+
+- **Research Topic**: GPS-denied UAV visual navigation with edge processing
+- **Sensitivity Level**: 🟠 High
+- **Rationale**: Deep learning feature matchers (SuperPoint, LightGlue, XFeat) and edge inference frameworks (TensorRT) evolve rapidly. Jetson Orin Nano Super is a recent (Dec 2024) product. Cross-view geo-localization is an active research area.
+- **Source Time Window**: 12 months (prioritize 2025-2026)
+- **Priority official sources to consult**:
+  1. NVIDIA Jetson documentation and benchmarks
+  2. OpenCV / kornia / hloc official docs
+  3. Recent papers on cross-view geo-localization (CVPR, ECCV, ICCV 2024-2025)
+- **Key version information to verify**:
+  - JetPack SDK: Current version ____
+  - SuperPoint/LightGlue: Latest available for TensorRT ____
+  - XFeat: Version and Jetson compatibility ____

_docs/00_research/gps_denied_nav/01_source_registry.md

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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**: 2024-08-22
+- **Timeliness Status**: ✅ Currently valid
+- **Target Audience**: Fixed-wing UAV GPS-denied navigation
+- **Research Boundary Match**: ✅ Full match
+- **Summary**: VO + satellite correction pipeline for fixed-wing UAV at 1000m+ altitude. Mean error 142.88m over 17km (0.83%). Uses ORB features, centroid-based displacement, Kalman filter smoothing, quadtree for satellite keypoint indexing.
+- **Related Sub-question**: A1, B5, C7, D11
+
+## Source #2
+- **Title**: SatLoc: Hierarchical Adaptive Fusion Framework for GNSS-denied UAV Localization
+- **Link**: https://www.scilit.com/publications/e5cafaf875a49297a62b298a89d5572f
+- **Tier**: L1
+- **Publication Date**: 2025
+- **Timeliness Status**: ✅ Currently valid
+- **Target Audience**: UAV localization in GNSS-denied environments
+- **Research Boundary Match**: ✅ Full match
+- **Summary**: Three-layer fusion: DinoV2 for satellite geo-localization, XFeat for VO, optical flow for velocity. Adaptive confidence-based weighting. <15m error, >90% coverage, 2+ Hz on edge hardware.
+- **Related Sub-question**: B4, B5, C8, D12
+
+## Source #3
+- **Title**: XFeat: Accelerated Features for Lightweight Image Matching (CVPR 2024)
+- **Link**: https://arxiv.org/abs/2404.19174
+- **Tier**: L1
+- **Publication Date**: 2024-04
+- **Timeliness Status**: ✅ Currently valid
+- **Target Audience**: Edge device feature matching
+- **Research Boundary Match**: ✅ Full match
+- **Summary**: 5x faster than SuperPoint, runs on CPU at VGA resolution. Sparse and semi-dense matching. TensorRT deployment available for Jetson. Comparable accuracy to SuperPoint.
+- **Related Sub-question**: C8, F18, F20
+
+## Source #4
+- **Title**: XFeat TensorRT Implementation
+- **Link**: https://github.com/PranavNedunghat/XFeatTensorRT
+- **Tier**: L2
+- **Publication Date**: 2024
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: C++ TensorRT implementation of XFeat, tested on Jetson Orin NX 16GB with JetPack 6.0, CUDA 12.2, TensorRT 8.6.
+- **Related Sub-question**: C8, F18, F19
+
+## Source #5
+- **Title**: SuperPoint+LightGlue TensorRT Deployment
+- **Link**: https://github.com/fettahyildizz/superpoint_lightglue_tensorrt
+- **Tier**: L2
+- **Publication Date**: 2024
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: C++ TensorRT implementation of SuperPoint+LightGlue. Production-ready deployment for Jetson platforms.
+- **Related Sub-question**: C8, F19
+
+## Source #6
+- **Title**: FP8 Quantized LightGlue in TensorRT
+- **Link**: https://fabio-sim.github.io/blog/fp8-quantized-lightglue-tensorrt-nvidia-model-optimizer/
+- **Tier**: L2
+- **Publication Date**: 2026
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: Up to ~6x speedup with FP8 quantization. Requires Hopper/Ada Lovelace GPUs (not available on Jetson Orin Nano Ampere). FP16 is the best available precision for Orin Nano.
+- **Related Sub-question**: F19
+
+## Source #7
+- **Title**: NVIDIA JetPack 6.2 Release Notes
+- **Link**: https://docs.nvidia.com/jetson/archives/jetpack-archived/jetpack-62/release-notes/index.html
+- **Tier**: L1
+- **Publication Date**: 2025
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: CUDA 12.6.10, TensorRT 10.3.0, cuDNN 9.3. Super Mode for Orin Nano: up to 2x inference performance, 50% memory bandwidth boost. Power modes: 15W, 25W, MAXN SUPER.
+- **Related Sub-question**: F18, F19, F20
+
+## Source #8
+- **Title**: cuda-efficient-features (GPU feature detection benchmarks)
+- **Link**: https://github.com/fixstars/cuda-efficient-features
+- **Tier**: L2
+- **Publication Date**: 2024
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: 4K detection: 12ms on Jetson Xavier. 8K: 27.5ms. 40K keypoints extraction: 20-25ms on Xavier. Orin Nano Super should be comparable or better.
+- **Related Sub-question**: F20
+
+## Source #9
+- **Title**: Adaptive Covariance Hybrid EKF/UKF for Visual-Inertial Odometry
+- **Link**: https://arxiv.org/abs/2512.17505
+- **Tier**: L1
+- **Publication Date**: 2025
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: Hybrid EKF/UKF achieves 49% better position accuracy, 57% better rotation accuracy than ESKF alone, at 48% lower computational cost than full UKF. Includes adaptive sensor confidence scoring.
+- **Related Sub-question**: E15
+
+## Source #10
+- **Title**: SIFT+LightGlue for UAV Image Mosaicking (ISPRS 2025)
+- **Link**: https://isprs-archives.copernicus.org/articles/XLVIII-2-W11-2025/169/2025/
+- **Tier**: L1
+- **Publication Date**: 2025
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: SIFT+LightGlue outperforms SuperPoint+LightGlue for UAV mosaicking across diverse scenarios. Superior in both low-texture and high-texture environments.
+- **Related Sub-question**: C8, D12
+
+## Source #11
+- **Title**: UAVision - GNSS-Denied UAV Visual Localization System
+- **Link**: https://github.com/ArboriseRS/UAVision
+- **Tier**: L4
+- **Publication Date**: 2024-2025
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: Open-source system using LightGlue for map matching. Includes image processing modules and visualization.
+- **Related Sub-question**: A2
+
+## Source #12
+- **Title**: TerboucheHacene/visual_localization
+- **Link**: https://github.com/TerboucheHacene/visual_localization
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: Vision-based GNSS-free localization with SuperPoint/SuperGlue/GIM matching. Optimized VO + satellite image matching hybrid pipeline. Learning-based matchers for natural environments.
+- **Related Sub-question**: A2, D12
+
+## Source #13
+- **Title**: GNSS-Denied Geolocalization with Terrain Constraints
+- **Link**: https://github.com/yfs90/gnss-denied-uav-geolocalization
+- **Tier**: L4
+- **Publication Date**: 2024
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: No altimeters/IMU required, uses image matching + terrain constraints. GPS-comparable accuracy for day/night across varied terrain.
+- **Related Sub-question**: A2
+
+## Source #14
+- **Title**: Google Maps Tile API Documentation
+- **Link**: https://developers.google.com/maps/documentation/tile/satellite
+- **Tier**: L1
+- **Publication Date**: Current
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: Zoom levels 0-22. Satellite tiles via HTTPS. Session tokens required. Bulk download possible but subject to usage policies.
+- **Related Sub-question**: D13
+
+## Source #15
+- **Title**: NaviLoc: Trajectory-Level Visual Localization for GNSS-Denied UAV Navigation
+- **Link**: https://www.mdpi.com/2504-446X/10/2/97
+- **Tier**: L1
+- **Publication Date**: 2025
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: Trajectory-level optimization rather than per-frame matching. Optimizes entire trajectory against satellite reference for improved accuracy.
+- **Related Sub-question**: B4, D11
+
+## Source #16
+- **Title**: GSD Estimation for UAV Photogrammetry
+- **Link**: https://blog.truegeometry.com/calculators/UAV_photogrammetry_workflows_calculation.html
+- **Tier**: L3
+- **Publication Date**: Current
+- **Timeliness Status**: ✅ Currently valid
+- **Summary**: GSD = (sensor_width × altitude) / (focal_length × image_width). For our case: (23.5mm × 400m) / (25mm × 6252) = 0.06 m/pixel.
+- **Related Sub-question**: C9

_docs/00_research/gps_denied_nav/02_fact_cards.md

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+# Fact Cards
+
+## Fact #1
+- **Statement**: XFeat achieves up to 5x faster inference than SuperPoint while maintaining comparable accuracy for pose estimation. It runs in real-time on CPU at VGA resolution.
+- **Source**: Source #3 (CVPR 2024 paper)
+- **Phase**: Phase 2
+- **Target Audience**: Edge device deployments
+- **Confidence**: ✅ High
+- **Related Dimension**: Feature Extraction
+
+## Fact #2
+- **Statement**: XFeat TensorRT implementation exists and is tested on Jetson Orin NX 16GB with JetPack 6.0, CUDA 12.2, TensorRT 8.6.
+- **Source**: Source #4
+- **Phase**: Phase 2
+- **Target Audience**: Jetson platform deployment
+- **Confidence**: ✅ High
+- **Related Dimension**: Feature Extraction, Edge Optimization
+
+## Fact #3
+- **Statement**: SatLoc framework achieves <15m absolute localization error with >90% trajectory coverage at 2+ Hz on edge hardware, using DinoV2 for satellite matching, XFeat for VO, and optical flow for velocity.
+- **Source**: Source #2
+- **Phase**: Phase 2
+- **Target Audience**: GNSS-denied UAV localization
+- **Confidence**: ⚠️ Medium (paper details not fully accessible)
+- **Related Dimension**: Overall Architecture, Accuracy
+
+## Fact #4
+- **Statement**: Mateos-Ramirez et al. achieved 142.88m mean error over 17km (0.83% error rate) with VO + satellite correction on a fixed-wing UAV at 1000m+ altitude. Without satellite correction, error accumulated to 850m+ over 17km.
+- **Source**: Source #1
+- **Phase**: Phase 2
+- **Target Audience**: Fixed-wing UAV at high altitude
+- **Confidence**: ✅ High
+- **Related Dimension**: Accuracy, Architecture
+
+## Fact #5
+- **Statement**: VIO systems typically drift 0.8-1% of distance traveled. Between satellite corrections at 1km intervals, expected drift is ~10m.
+- **Source**: Multiple sources (arxiv VIO benchmarks)
+- **Phase**: Phase 2
+- **Target Audience**: Aerial VIO systems
+- **Confidence**: ✅ High
+- **Related Dimension**: VO Drift
+
+## Fact #6
+- **Statement**: Jetson Orin Nano Super: 8GB LPDDR5 shared memory, 1024 CUDA cores, 32 Tensor Cores, 102 GB/s bandwidth, 67 TOPS INT8. JetPack 6.2: CUDA 12.6.10, TensorRT 10.3.0.
+- **Source**: Source #7
+- **Phase**: Phase 2
+- **Target Audience**: Hardware specification
+- **Confidence**: ✅ High
+- **Related Dimension**: Edge Optimization
+
+## Fact #7
+- **Statement**: CUDA-accelerated feature detection at 4K (3840x2160): ~12ms on Jetson Xavier. At 8K: ~27.5ms. Descriptor extraction for 40K keypoints: ~20-25ms on Xavier. Orin Nano Super has comparable or slightly better compute.
+- **Source**: Source #8
+- **Phase**: Phase 2
+- **Target Audience**: Jetson GPU performance
+- **Confidence**: ✅ High
+- **Related Dimension**: Processing Time
+
+## Fact #8
+- **Statement**: Hybrid EKF/UKF achieves 49% better position accuracy than ESKF alone at 48% lower computational cost than full UKF. Includes adaptive sensor confidence scoring based on image entropy and motion blur.
+- **Source**: Source #9
+- **Phase**: Phase 2
+- **Target Audience**: VIO fusion
+- **Confidence**: ✅ High
+- **Related Dimension**: Sensor Fusion
+
+## Fact #9
+- **Statement**: SIFT+LightGlue outperforms SuperPoint+LightGlue for UAV mosaicking across diverse scenarios (low-texture agricultural and high-texture urban).
+- **Source**: Source #10
+- **Phase**: Phase 2
+- **Target Audience**: UAV image matching
+- **Confidence**: ✅ High
+- **Related Dimension**: Feature Matching
+
+## Fact #10
+- **Statement**: GSD for our system at 400m: (23.5mm × 400m) / (25mm × 6252px) = 0.060 m/pixel. Image footprint: 6252 × 0.06 = 375m width, 4168 × 0.06 = 250m height.
+- **Source**: Source #16 + camera parameters
+- **Phase**: Phase 2
+- **Target Audience**: Our specific system
+- **Confidence**: ✅ High
+- **Related Dimension**: Scale Estimation
+
+## Fact #11
+- **Statement**: Google Maps satellite tiles available via Tile API at zoom levels 0-22. Max zoom varies by region. For eastern Ukraine, zoom 18 (~0.6 m/px) is typically available; zoom 19 (~0.3 m/px) may not be.
+- **Source**: Source #14
+- **Phase**: Phase 2
+- **Target Audience**: Satellite imagery
+- **Confidence**: ⚠️ Medium (exact zoom availability for eastern Ukraine unverified)
+- **Related Dimension**: Satellite Reference
+
+## Fact #12
+- **Statement**: FP8 quantization for LightGlue requires Hopper/Ada GPUs. Jetson Orin Nano uses Ampere architecture — limited to FP16 as best TensorRT precision.
+- **Source**: Source #6, Source #7
+- **Phase**: Phase 2
+- **Target Audience**: Jetson optimization
+- **Confidence**: ✅ High
+- **Related Dimension**: Edge Optimization
+
+## Fact #13
+- **Statement**: SuperPoint+LightGlue TensorRT C++ deployment is available and production-tested. ONNX Runtime path achieves 2-4x speedup over compiled PyTorch.
+- **Source**: Source #5, Source #6
+- **Phase**: Phase 2
+- **Target Audience**: Production deployment
+- **Confidence**: ✅ High
+- **Related Dimension**: Feature Matching, Edge Optimization
+
+## Fact #14
+- **Statement**: Cross-view matching (UAV-to-satellite) is fundamentally harder than same-view matching due to extreme viewpoint differences. Deep learning embeddings (DinoV2, CLIP-based) are the state-of-the-art for coarse retrieval. Local features are used for fine alignment.
+- **Source**: Multiple (Sources #2, #12, #15)
+- **Phase**: Phase 2
+- **Target Audience**: Cross-view geo-localization
+- **Confidence**: ✅ High
+- **Related Dimension**: Satellite Matching
+
+## Fact #15
+- **Statement**: Quadtree spatial indexing enables O(log n) nearest-neighbor lookup for satellite keypoints. Combined with GeoHash for fast region encoding, this is the standard approach for tile management.
+- **Source**: Sources #1, #14
+- **Phase**: Phase 2
+- **Target Audience**: Spatial indexing
+- **Confidence**: ✅ High
+- **Related Dimension**: Satellite Tile Management

_docs/00_research/gps_denied_nav/03_comparison_framework.md

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+# Comparison Framework
+
+## Selected Framework Type
+Decision Support — evaluating solution options per component
+
+## Architecture Components to Evaluate
+
+1. Feature Extraction & Matching (VO frame-to-frame)
+2. Satellite Image Matching (cross-view geo-registration)
+3. Sensor Fusion (VO + satellite + IMU)
+4. Satellite Tile Preprocessing & Indexing
+5. Image Downsampling Strategy
+6. Re-localization (disconnected segments)
+7. API & Streaming Layer
+
+## Component 1: Feature Extraction & Matching (VO)
+
+| Dimension | XFeat | SuperPoint + LightGlue | ORB (OpenCV) |
+|-----------|-------|----------------------|--------------|
+| Speed (Jetson) | ~2-5ms per frame (VGA), 5x faster than SuperPoint | ~15-50ms per frame (VGA, TensorRT FP16) | ~5-10ms per frame (CUDA) |
+| Accuracy | Comparable to SuperPoint on pose estimation | State-of-the-art for local features | Lower accuracy, not scale-invariant |
+| Memory | <100MB model | ~200-400MB model+inference | Negligible |
+| TensorRT support | Yes (C++ impl available for Jetson Orin NX) | Yes (C++ impl available) | N/A (native CUDA) |
+| Cross-view capability | Limited (same-view designed) | Better with LightGlue attention | Poor for cross-view |
+| Rotation invariance | Moderate | Good with LightGlue | Good (by design) |
+| Jetson validation | Tested on Orin NX (JetPack 6.0) | Tested on multiple Jetson platforms | Native OpenCV CUDA |
+| **Fit for VO** | ✅ Best — fast, accurate, Jetson-proven | ⚠️ Good but heavier | ⚠️ Fast but less accurate |
+| **Fit for satellite matching** | ⚠️ Moderate | ✅ Better for cross-view with attention | ❌ Poor for cross-view |
+
+## Component 2: Satellite Image Matching (Cross-View)
+
+| Dimension | Local Feature Matching (SIFT/SuperPoint + LightGlue) | Global Descriptor Retrieval (DinoV2/CLIP) | Template Matching (NCC) |
+|-----------|-----------------------------------------------------|------------------------------------------|------------------------|
+| Approach | Extract keypoints in both UAV and satellite images, match descriptors | Encode both images into global vectors, compare by distance | Slide UAV image over satellite tile, compute correlation |
+| Accuracy | Sub-pixel when matches found (best for fine alignment) | Tile-level (~50-200m depending on tile size) | Pixel-level but sensitive to appearance changes |
+| Speed | ~100-500ms for match+geometric verification | ~50-100ms for descriptor comparison | ~500ms-2s for large search area |
+| Robustness to viewpoint | Good with LightGlue attention | Excellent (trained for cross-view) | Poor (requires similar viewpoint) |
+| Memory | ~300-500MB (model + keypoints) | ~200-500MB (model) | Low |
+| Failure rate | High in low-texture, seasonal changes | Lower — semantic understanding | High in changed scenes |
+| **Recommended role** | Fine alignment (after coarse retrieval) | Coarse retrieval (select candidate tile) | Not recommended |
+
+## Component 3: Sensor Fusion
+
+| Dimension | EKF (Extended Kalman Filter) | Error-State EKF (ESKF) | Hybrid ESKF/UKF | Factor Graph (GTSAM) |
+|-----------|-------------------------------|------------------------|------------------|---------------------|
+| Accuracy | Baseline | Better for rotation | 49% better than ESKF | Best overall |
+| Compute cost | Lowest | Low | 48% less than full UKF | Highest |
+| Implementation complexity | Low | Medium | Medium-High | High |
+| Handles non-linearity | Linearization errors | Better for small errors | Best among KF variants | Full non-linear |
+| Real-time on Jetson | ✅ | ✅ | ✅ | ⚠️ Depends on graph size |
+| Multi-rate sensor support | Manual | Manual | Manual | Native |
+| **Fit** | ⚠️ Baseline option | ✅ Good starting point | ✅ Best KF option | ⚠️ Overkill for this system |
+
+## Component 4: Satellite Tile Management
+
+| Dimension | GeoHash + In-Memory | Quadtree + Memory-Mapped Files | Pre-extracted Feature DB |
+|-----------|--------------------|-----------------------------|------------------------|
+| Lookup speed | O(1) hash | O(log n) tree traversal | O(1) hash + feature load |
+| Memory usage | All tiles in RAM | On-demand loading | Features only (smaller) |
+| Preprocessing | Fast | Moderate | Slow (extract all features offline) |
+| Flexibility | Fixed grid | Adaptive resolution | Fixed per-tile |
+| **Fit for 8GB** | ❌ Too much RAM for large areas | ✅ Memory-efficient | ✅ Best — smallest footprint |
+
+## Component 5: Image Downsampling Strategy
+
+| Dimension | Fixed Resize (e.g., 1600x1066) | Pyramid (multi-scale) | ROI-based (center crop + full) |
+|-----------|-------------------------------|----------------------|-------------------------------|
+| Speed | Fast, single scale | Slower, multiple passes | Medium |
+| Accuracy | Good if GSD ratio maintained | Best for multi-scale features | Good for center, loses edges |
+| Memory | ~5MB per frame | ~7-8MB per frame | ~6MB per frame |
+| **Fit** | ✅ Best tradeoff | ⚠️ Unnecessary complexity | ⚠️ Loses coverage |

_docs/00_research/gps_denied_nav/04_reasoning_chain.md

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+# Reasoning Chain
+
+## Dimension 1: Feature Extraction for Visual Odometry
+
+### Fact Confirmation
+XFeat is 5x faster than SuperPoint (Fact #1), has TensorRT deployment on Jetson (Fact #2), and comparable accuracy for pose estimation. SatLoc (the most relevant state-of-the-art system) uses XFeat for its VO component (Fact #3).
+
+### Reference Comparison
+SuperPoint+LightGlue is more accurate for cross-view matching but heavier. ORB is fast but less accurate and not robust to appearance changes. SIFT+LightGlue is best for mosaicking (Fact #9) but slower.
+
+### Conclusion
+**XFeat for VO (frame-to-frame)** — it's the fastest learned feature, Jetson-proven, and used by the closest state-of-the-art system (SatLoc). For satellite matching, a different approach is needed because cross-view matching requires viewpoint-invariant features.
+
+### Confidence
+✅ High — supported by SatLoc architecture and CVPR 2024 benchmarks.
+
+---
+
+## Dimension 2: Satellite Image Matching Strategy
+
+### Fact Confirmation
+Cross-view matching is fundamentally harder than same-view (Fact #14). Deep learning embeddings (DinoV2) are state-of-the-art for coarse retrieval (Fact #3). Local features are better for fine alignment. SatLoc uses DinoV2 for satellite matching specifically.
+
+### Reference Comparison
+A two-stage coarse-to-fine approach is the dominant pattern in literature: (1) global descriptor retrieves candidate region, (2) local feature matching refines position. Pure local-feature matching has high failure rate for cross-view due to extreme viewpoint differences.
+
+### Conclusion
+**Two-stage approach**: (1) Coarse — use a lightweight global descriptor to find the best-matching satellite tile within the search area (VO-predicted position ± uncertainty radius). (2) Fine — use local feature matching (SuperPoint+LightGlue or XFeat) between UAV frame and the matched satellite tile to get precise position. The coarse stage can also serve as the re-localization mechanism for disconnected segments.
+
+### Confidence
+✅ High — consensus across multiple recent papers and the SatLoc system.
+
+---
+
+## Dimension 3: Sensor Fusion Approach
+
+### Fact Confirmation
+Hybrid ESKF/UKF achieves 49% better accuracy than ESKF alone at 48% lower cost than full UKF (Fact #8). Factor graphs (GTSAM) offer the best accuracy but are computationally expensive.
+
+### Reference Comparison
+For our system: IMU runs at 100-400Hz, VO at ~3Hz (frame rate), satellite corrections at variable rate (whenever matching succeeds). We need multi-rate fusion that handles intermittent satellite corrections and continuous IMU.
+
+### Conclusion
+**Error-State EKF (ESKF)** as the baseline fusion approach — it's well-understood, lightweight, handles multi-rate sensors naturally, and is proven for VIO on edge hardware. Upgrade to hybrid ESKF/UKF if ESKF accuracy is insufficient. Factor graphs are overkill for this real-time edge system.
+
+The filter state: position (lat/lon), velocity, orientation (quaternion), IMU biases. Measurements: VO-derived displacement (high rate), satellite-derived absolute position (variable rate), IMU (highest rate for prediction).
+
+### Confidence
+✅ High — ESKF is the standard choice for embedded VIO systems.
+
+---
+
+## Dimension 4: Satellite Tile Preprocessing & Indexing
+
+### Fact Confirmation
+Quadtree enables O(log n) lookups (Fact #15). Pre-extracting features offline saves runtime compute. 8GB memory limits in-memory tile storage.
+
+### Reference Comparison
+Full tiles in memory is infeasible for large areas. Memory-mapped files allow on-demand loading. Pre-extracted feature databases have the smallest runtime footprint.
+
+### Conclusion
+**Offline preprocessing pipeline**:
+1. Download Google Maps satellite tiles at max zoom (18-19) for the operational area
+2. Extract features (XFeat or SuperPoint) from each tile
+3. Compute global descriptors (lightweight, e.g., NetVLAD or cosine-pooled XFeat descriptors) per tile
+4. Store: tile metadata (GPS bounds, zoom level), features + descriptors in a GeoHash-indexed database
+5. Build spatial index (GeoHash) for fast lookup by GPS region
+
+**Runtime**: Given VO-estimated position, query GeoHash to find nearby tiles, compare global descriptors for coarse match, then local feature matching for fine alignment.
+
+### Confidence
+✅ High — standard approach used by all relevant systems.
+
+---
+
+## Dimension 5: Image Downsampling Strategy
+
+### Fact Confirmation
+26MP images need downsampling for 8GB device (Fact #6). Feature extraction at 4K takes ~12ms on Jetson Xavier (Fact #7). UAV GSD at 400m is ~6cm/px (Fact #10). Satellite GSD is ~60cm/px at zoom 18.
+
+### Reference Comparison
+For VO (frame-to-frame): features at full resolution are wasteful — consecutive frames at 6cm GSD overlap ~80%, and features at lower resolution are sufficient for displacement estimation. For satellite matching: we need to match at satellite resolution (~60cm/px), so downsampling to match satellite GSD is natural.
+
+### Conclusion
+**Downsample to ~1600x1066** (factor ~4x each dimension). This yields ~24cm/px GSD — still 2.5x finer than satellite, sufficient for feature matching. Image size: ~5MB (RGB). Feature extraction at this resolution: <10ms. This is the single resolution for both VO and satellite matching.
+
+### Confidence
+✅ High — standard practice for edge processing of high-res imagery.
+
+---
+
+## Dimension 6: Disconnected Segment Handling
+
+### Fact Confirmation
+SatLoc uses satellite matching as an independent localization source that works regardless of VO state (Fact #3). The AC requires reconnecting disconnected segments as a core capability.
+
+### Reference Comparison
+Pure VO cannot handle zero-overlap transitions. IMU dead-reckoning bridges short gaps (seconds). Satellite-based re-localization provides absolute position regardless of VO state.
+
+### Conclusion
+**Independent satellite localization per frame** — every frame attempts satellite matching regardless of VO state. This naturally handles disconnected segments:
+1. When VO succeeds: satellite matching refines position (high confidence)
+2. When VO fails (sharp turn): satellite matching provides absolute position (sole source)
+3. When both fail: IMU dead-reckoning with low confidence score
+4. After 3 consecutive total failures: request user input
+
+Segment reconnection is automatic: all positions are in the same global (WGS84) frame via satellite matching. No explicit "reconnection" needed — segments share the satellite reference.
+
+### Confidence
+✅ High — this is the key architectural insight.
+
+---
+
+## Dimension 7: Processing Pipeline Architecture
+
+### Fact Confirmation
+<5s per frame required (AC). Feature extraction ~10ms, VO matching ~20-50ms, satellite coarse retrieval ~50-100ms, satellite fine matching ~200-500ms, fusion ~1ms. Total: ~300-700ms per frame.
+
+### Conclusion
+**Pipelined parallel architecture**:
+- Thread 1 (Camera): Capture frame, downsample, extract features → push to queue
+- Thread 2 (VO): Match with previous frame, compute displacement → push to fusion
+- Thread 3 (Satellite): Search nearby tiles, coarse retrieval, fine matching → push to fusion
+- Thread 4 (Fusion): ESKF prediction (IMU), update (VO), update (satellite) → emit result via SSE
+
+VO and satellite matching can run in parallel for each frame. Fusion integrates results as they arrive. This enables <1s per frame total latency.
+
+### Confidence
+✅ High — standard producer-consumer pipeline.

_docs/00_research/gps_denied_nav/05_validation_log.md

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+# Validation Log
+
+## Validation Scenario 1: Normal Flight (80% of time)
+UAV flies straight, consecutive frames overlap ~70-80%. Terrain has moderate texture (agricultural + urban mix).
+
+### Expected Based on Conclusions
+- XFeat extracts features in ~5ms, VO matching in ~20ms
+- Satellite matching succeeds: coarse retrieval ~50ms, fine matching ~300ms
+- ESKF fuses both: position accuracy ~10-20m (satellite-anchored)
+- Total processing: <500ms per frame
+- Confidence: HIGH
+
+### Actual Validation (against literature)
+SatLoc reports <15m error with >90% coverage under similar conditions. Mateos-Ramirez reports 0.83% drift with satellite correction. Both align with our expected performance.
+
+### Result: ✅ PASS
+
+---
+
+## Validation Scenario 2: Sharp Turn (5-10% of time)
+UAV makes a 60-degree turn. Next frame has <5% overlap with previous. Heading changes rapidly.
+
+### Expected Based on Conclusions
+- VO fails (insufficient feature overlap) — detected by low match count
+- IMU provides heading and approximate displacement for ~1-2 frames
+- Satellite matching attempts independent localization of the new frame
+- If satellite match succeeds: position recovered, segment continues
+- If satellite match fails: IMU dead-reckoning with LOW confidence
+
+### Potential Issues
+- Satellite matching may also fail if the frame is heavily tilted (non-nadir view during turn)
+- IMU drift during turn: at 100m/s for 1s, displacement ~100m. IMU drift over 1s: ~1-5m — acceptable
+
+### Result: ⚠️ CONDITIONAL PASS — depends on satellite matching success during turn. Non-stabilized camera may produce tilted images that are harder to match. IMU provides reasonable bridge.
+
+---
+
+## Validation Scenario 3: Disconnected Route (rare, <5%)
+UAV completes segment A, makes a 90+ degree turn, flies a new heading. Segment B has no overlap with segment A. Multiple such segments possible.
+
+### Expected Based on Conclusions
+- Each segment independently localizes via satellite matching
+- No explicit reconnection needed — all in WGS84 frame
+- Per-segment accuracy depends on satellite matching success rate
+- Low-confidence gaps between segments until satellite match succeeds
+
+### Result: ✅ PASS — architecture handles this natively via independent per-frame satellite matching.
+
+---
+
+## Validation Scenario 4: Memory-Constrained Operation (always)
+3000 frames, 8GB shared memory. Full pipeline running.
+
+### Expected Based on Conclusions
+- Downsampled frame: ~5MB per frame. Keep 2 in memory (current + previous): ~10MB
+- XFeat model (TensorRT): ~50-100MB
+- Satellite tile features (loaded tiles): ~200-500MB for tiles near current position
+- ESKF state: <1MB
+- OS + runtime: ~1.5GB
+- Total: ~2-3GB active, well within 8GB
+
+### Potential Issues
+- Satellite feature DB for large operational areas could be large on disk (not memory — loaded on demand)
+- Need careful management of tile loading/unloading
+
+### Result: ✅ PASS — 8GB is sufficient with proper memory management.
+
+---
+
+## Validation Scenario 5: Degraded Satellite Imagery
+Google Maps tiles at 0.5-1.0 m/px resolution. Some areas have outdated imagery. Seasonal appearance changes.
+
+### Expected Based on Conclusions
+- Coarse retrieval (global descriptors) should handle moderate appearance changes
+- Fine matching may fail on outdated/seasonal tiles — confidence drops to LOW
+- System falls back to VO + IMU in degraded areas
+- Multiple consecutive failures → user input request
+
+### Potential Issues
+- If large areas have degraded satellite imagery, the system may operate mostly in VO+IMU mode with significant drift
+- 50m accuracy target may not be achievable in these areas
+
+### Result: ⚠️ CONDITIONAL PASS — system degrades gracefully, but accuracy targets depend on satellite quality. This is a known risk per Phase 1 assessment.
+
+---
+
+## Review Checklist
+- [x] Draft conclusions consistent with fact cards
+- [x] No important dimensions missed
+- [x] No over-extrapolation
+- [x] Conclusions actionable/verifiable
+- [x] Sharp turn handling addressed
+- [x] Memory constraints validated
+- [ ] Issue: Satellite imagery quality in eastern Ukraine remains a risk
+- [ ] Issue: Non-stabilized camera during turns may degrade satellite matching
+
+## Conclusions Requiring No Revision
+All major architectural decisions validated. Two known risks (satellite quality, non-stabilized camera during turns) are acknowledged and handled by the fallback hierarchy.