# Solution Draft

## Assessment Findings

| Old Component Solution | Weak Point | New Solution |
|------------------------|------------|-------------|
| Direct SuperPoint+LightGlue satellite matching | **Functional**: No coarse localization stage. Fails when VO drift is large or satellite tile is wrong. Not rotation-invariant (GitHub issue #64). Returns false matches on non-overlapping pairs (issue #13). | Two-stage: DINOv2 coarse retrieval → SuperPoint+LightGlue fine alignment. Image rotation normalization. Geometric consistency check for match validation. |
| SuperPoint for all feature extraction | **Performance**: Unified pipeline is simpler but suboptimal. SuperPoint ~80ms per image is slower than needed for every-frame VO. | Dual-extractor: XFeat for VO (5x faster, ~15ms), SuperPoint for satellite matching (higher accuracy). |
| scipy.optimize sliding window | **Functional**: Generic optimizer. No proper uncertainty modeling per measurement. No terrain constraints. Reinvents what GTSAM already provides. | GTSAM iSAM2 factor graph: BetweenFactor (VO), GPSFactor (satellite anchors), terrain constraints from Copernicus DEM. |
| Google Maps as sole satellite provider | **Functional**: Eastern Ukraine imagery 3-5+ years old. $200/month free credit expired Feb 2025. 15K/day rate limit tight for large flights. | Multi-provider: Google Maps primary + Mapbox fallback + user-provided tiles. Request budgeting. |
| No image downscaling strategy | **Performance/Memory**: 6252×4168 images cannot fit in 6GB VRAM for feature extraction. No memory budget specified. | Downscale to 1600 long edge for feature extraction. Streaming one-at-a-time processing. Explicit memory budgets. |
| No camera rotation handling | **Functional**: Non-stabilized camera produces rotated images. SuperPoint/LightGlue fail at 90° rotation. | Estimate heading from VO chain. Rectify images before satellite matching. SIFT fallback for rotation-heavy cases. |
| Homography VO without terrain correction | **Functional**: GSD assumes constant altitude. No fallback for non-planar scenes. Decomposition can be unstable. | Integrate Copernicus DEM for terrain-corrected GSD. Essential matrix fallback when RANSAC inlier ratio is low. |
| No non-match detection | **Functional**: VO failure detection relies on match count only. Misses geometrically inconsistent matches. | Triple check: match count + RANSAC inlier ratio + motion consistency with previous frames. |
| API key authentication only | **Security**: API keys in URLs persist in logs and browser history. No SSE connection limits. No DoS protection. | JWT authentication. Short-lived SSE tokens. Rate limiting. Connection pool limits. Image size validation. |
| Segment reconnection via satellite only | **Functional**: Floating segments with no satellite match stay permanently unresolved. | Cross-segment matching when new anchors arrive. DEM constraints. Configurable user-input timeout with auto-continue. |

## Product Solution Description

A Python-based GPS-denied visual navigation service that determines GPS coordinates of consecutive UAV photo centers using a hierarchical localization approach: fast visual odometry for frame-to-frame motion, two-stage satellite geo-referencing (coarse retrieval + fine matching) for absolute positioning, and factor graph optimization for trajectory refinement. The system operates as a background REST API service with real-time SSE streaming.

**Core approach**: Consecutive images are matched using XFeat (fast learned features) to estimate relative motion (visual odometry). Periodically, each image is geo-referenced against satellite imagery through a two-stage process: DINOv2 global retrieval selects the best-matching satellite tile, then SuperPoint+LightGlue refines the alignment to pixel precision. A GTSAM iSAM2 factor graph fuses VO constraints, satellite anchors, and DEM terrain constraints to produce an optimized trajectory. The system handles route disconnections by treating each continuous VO chain as an independent segment, geo-referenced through satellite matching and connected via the shared WGS84 coordinate frame.

```
┌─────────────────────────────────────────────────────────────────────┐
│                        Client (Desktop App)                         │
│   POST /jobs (start GPS, camera params, image folder)               │
│   GET  /jobs/{id}/stream (SSE)                                      │
│   POST /jobs/{id}/anchor (user manual GPS input)                    │
│   GET  /jobs/{id}/point-to-gps (image_id, pixel_x, pixel_y)        │
└──────────────────────┬──────────────────────────────────────────────┘
                       │ HTTP/SSE (JWT auth)
┌──────────────────────▼──────────────────────────────────────────────┐
│                     FastAPI Service Layer                            │
│   Job Manager → Pipeline Orchestrator → SSE Event Emitter           │
└──────────────────────┬──────────────────────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────────────────────┐
│                     Processing Pipeline                              │
│                                                                      │
│  ┌──────────────┐  ┌──────────────┐  ┌─────────────────────────┐   │
│  │ Image        │  │  Visual      │  │ Satellite Geo-Ref       │   │
│  │ Preprocessor │→│  Odometry    │→│ Stage 1: DINOv2 retrieval│   │
│  │ (downscale,  │  │ (XFeat +    │  │ Stage 2: SuperPoint +   │   │
│  │  rectify)    │  │  LightGlue) │  │   LightGlue refinement  │   │
│  └──────────────┘  └──────────────┘  └─────────────────────────┘   │
│         │                │                       │                   │
│         ▼                ▼                       ▼                   │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │       GTSAM iSAM2 Factor Graph Optimizer                    │   │
│  │  (VO factors + satellite anchors + DEM terrain constraints)  │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                              │                                       │
│  ┌───────────────────────────▼──────────────────────────────────┐   │
│  │                  Segment Manager                              │   │
│  │   (independent segments, cross-segment reconnection)          │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                      │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │          Multi-Provider Satellite Tile Cache                  │   │
│  │  (Google Maps + Mapbox + user tiles, disk cache, DEM cache)   │   │
│  └──────────────────────────────────────────────────────────────┘   │
└──────────────────────────────────────────────────────────────────────┘
```

## Architecture

### Component: Image Preprocessor

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| Downscale + rectify pipeline | OpenCV resize, NumPy | Normalizes input for all downstream components. Consistent memory usage. Preserves full-res metadata for GSD. | Loses fine detail in downscaled images. | OpenCV, NumPy | Input validation on image files | <10ms per image | **Best** |

**Selected**: Downscale + rectify pipeline.

**Preprocessing per image**:
1. Load image, validate format and dimensions
2. Downscale to max 1600 pixels on longest edge (preserving aspect ratio) for feature extraction
3. Store original resolution for GSD calculation: `GSD = (altitude × sensor_width) / (focal_length × original_width)`
4. If estimated heading is available (from previous VO): rotate image to approximate north-up orientation for satellite matching
5. Convert to grayscale for feature extraction
6. Output: downscaled grayscale image + metadata (original dims, GSD, estimated heading)

### Component: Feature Extraction

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| XFeat (for VO) | accelerated_features (PyTorch) | 5x faster than SuperPoint. CPU-capable. Sparse + semi-dense matching. Used in SatLoc-Fusion. | Fewer keypoints than SuperPoint in some scenes. | PyTorch | Model weights from official source | ~15ms GPU, ~50ms CPU | **Best for VO** |
| SuperPoint (for satellite matching) | superpoint (PyTorch) | Learned features, robust to viewpoint/illumination. Proven for satellite matching (ISPRS 2025). 256-dim descriptors. | Slower than XFeat. Not rotation-invariant. | NVIDIA GPU, PyTorch, CUDA | Model weights from official source | ~80ms GPU | **Best for satellite** |
| SIFT (fallback) | OpenCV cv2.SIFT | Rotation-invariant. Scale-invariant. Better for high-rotation scenarios. | Slower. Less discriminative in low-texture. | OpenCV | N/A | ~200ms CPU | Rotation fallback |

**Selected**: XFeat for VO, SuperPoint for satellite matching, SIFT as rotation-heavy fallback.

### Component: Feature Matching

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| LightGlue + ONNX/TensorRT | lightglue + LightGlue-ONNX | 2-4x faster than PyTorch via ONNX. Best balance of speed/accuracy. FlashAttention-2 + TopK-trick for additional 30%. | FP8 not available on RTX 2060 (Turing). Not rotation-invariant. | NVIDIA GPU, ONNX Runtime / TensorRT | Model weights from official source | ~50-100ms ONNX on RTX 2060 | **Best** |
| SuperGlue | superglue (PyTorch) | Strong spatial context. 93% match rate. | 2x slower than LightGlue. Non-commercial license. | NVIDIA GPU, PyTorch | Model weights from official source | ~100-200ms | Backup |
| DALGlue | dalglue (PyTorch) | 11.8% MMA improvement over LightGlue. UAV-optimized wavelet preprocessing. | Very new (2025). Limited production validation. | NVIDIA GPU, PyTorch | Model weights from official source | Comparable to LightGlue | Monitor for future |

**Selected**: LightGlue with ONNX optimization. DALGlue to evaluate when mature.

### Component: Visual Odometry (Consecutive Frame Matching)

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| Homography VO with essential matrix fallback | OpenCV findHomography, findEssentialMat, decomposeHomographyMat | Homography: optimal for flat terrain. Essential matrix: handles non-planar. Known altitude resolves scale. | Homography assumes planar. Essential matrix: more complex, scale ambiguity. | OpenCV, NumPy | N/A | ~5ms for estimation | **Best** |
| ORB-SLAM3 monocular | ORB-SLAM3 | Full SLAM with loop closure. | Heavy. Map building unnecessary. C++ dependency. | ROS (optional), C++ | N/A | — | Over-engineered |

**Selected**: Homography VO with essential matrix fallback and DEM terrain correction.

**VO Pipeline per frame**:
1. Extract XFeat features from current image (~15ms)
2. Match with previous image using LightGlue ONNX (~50ms)
3. **Triple failure check**: match count ≥ 30 AND RANSAC inlier ratio ≥ 0.4 AND motion magnitude consistent with expected inter-frame distance (100m ± 250m to handle outliers up to 350m)
4. If checks pass → estimate homography (cv2.findHomography with USAC_MAGSAC)
5. If RANSAC inlier ratio < 0.6 → additionally estimate essential matrix (cv2.findEssentialMat) as quality check
6. Decompose homography → rotation + translation
7. Select correct decomposition (motion consistent with previous direction + positive depth)
8. **Terrain-corrected GSD**: query Copernicus DEM at estimated position → `effective_altitude = flight_altitude - terrain_elevation` → `GSD = (effective_altitude × sensor_width) / (focal_length × original_image_width)`
9. Convert pixel displacement to meters: `displacement_m = displacement_px × GSD`
10. Update position: `new_pos = prev_pos + rotation @ displacement_m`
11. Track cumulative heading for image rectification
12. If triple failure check fails → trigger segment break

### Component: Satellite Image Geo-Referencing (Two-Stage)

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| Stage 1: DINOv2 coarse retrieval | dinov2 (PyTorch), faiss | Handles large viewpoint/domain gap. Finds correct area even with 100-200m VO drift. Semantic matching robust to seasonal change. | Coarse only (~tile-level). Needs pre-computed satellite tile embeddings. | PyTorch, faiss | Model weights from official source | ~50ms per query | **Best coarse** |
| Stage 2: SuperPoint+LightGlue fine matching with perspective warping | SuperPoint, LightGlue-ONNX, OpenCV warpPerspective | Precise alignment. Pixel-level accuracy. Proven on satellite benchmarks. | Needs rough pose for warping. Fails without Stage 1 on large drift. | PyTorch, ONNX Runtime, OpenCV | API keys secured | ~150ms total | **Best fine** |

**Selected**: Two-stage hierarchical matching.

**Satellite Matching Pipeline**:
1. Estimate approximate position from VO
2. **Stage 1 — Coarse retrieval**:
   a. Define search area: 500m radius around VO estimate (expand to 1km if segment just started)
   b. Pre-compute DINOv2 embeddings for all satellite tiles in search area (cached)
   c. Extract DINOv2 embedding from rectified UAV image
   d. Find top-5 most similar satellite tiles using faiss cosine similarity
3. **Stage 2 — Fine matching** (on top-5 tiles, stop on first good match):
   a. Warp UAV image to approximate nadir view using estimated camera pose
   b. Extract SuperPoint features from warped UAV image
   c. Extract SuperPoint features from satellite tile (pre-computed and cached)
   d. Match with LightGlue ONNX
   e. **Geometric validation**: require ≥15 inliers, inlier ratio ≥ 0.3, reprojection error < 3px
   f. If valid: estimate homography → transform image center → satellite pixel → WGS84
   g. Report: absolute position anchor with confidence based on match quality
4. If all 5 tiles fail Stage 2: try SIFT+LightGlue (rotation-invariant), try zoom level 17 (wider view)
5. If still fails: mark frame as VO-only, reduce confidence, continue

**Satellite matching frequency**: Every frame when available, but async — don't block VO pipeline. Satellite result arrives and gets added to factor graph retroactively.

### Component: GTSAM Factor Graph Optimizer

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| GTSAM iSAM2 factor graph | gtsam 4.2 (pip) | Incremental smoothing. Proper uncertainty propagation. Built-in GPSFactor. Backward smoothing on new evidence. Python bindings. Production-proven. | C++ backend (pip binary handles this). Learning curve for factor graph API. | gtsam==4.2, NumPy | N/A | ~5-10ms incremental update | **Best** |
| scipy.optimize sliding window | scipy, NumPy | Simple. No external dependency. | Generic optimizer. No uncertainty. No incremental update. | SciPy, NumPy | N/A | ~10-50ms per window | Baseline only |

**Selected**: GTSAM iSAM2.

**Factor graph structure**:
- **Variables**: Pose2 (x, y, heading) per image
- **VO Factor** (BetweenFactorPose2): relative motion between consecutive frames. Noise model: diagonal with sigma proportional to `1 / inlier_ratio`. Higher inlier ratio = lower uncertainty.
- **Satellite Anchor Factor** (GPSFactor or PriorFactorPoint2): absolute position from satellite matching. Noise model: sigma proportional to `reprojection_error × GSD`. Good match (~0.5px × 0.4m/px) = 0.2m sigma. Poor match = 5-10m sigma.
- **DEM Terrain Factor** (custom): constrains altitude to be consistent with Copernicus DEM at estimated position. Soft constraint, sigma = 5m.
- **Drift Limit Factor** (custom): penalizes cumulative VO displacement between satellite anchors exceeding 100m. Activated only when two anchor-to-anchor VO path exceeds threshold.

**Optimizer behavior**:
- On each new frame: add VO factor, run iSAM2.update() → ~5ms
- On satellite match arrival: add anchor factor, run iSAM2.update() → triggers backward correction of recent poses
- Emit updated positions via SSE after each update
- Refinement events: when backward correction moves positions by >1m, emit "refined" SSE event

### Component: Segment Manager

The segment manager tracks independent VO chains and manages their lifecycle and interconnection.

**Segment lifecycle**:
1. **Start condition**: First image, OR VO triple failure check fails
2. **Active tracking**: VO provides frame-to-frame motion within segment
3. **Anchoring**: Satellite two-stage matching provides absolute position
4. **End condition**: VO failure (sharp turn, outlier >350m, occlusion)
5. **New segment**: Starts from satellite anchor or user GPS

**Segment states**:
- `ANCHORED`: At least one satellite match → HIGH confidence
- `FLOATING`: No satellite match yet → positioned relative to start point → LOW confidence
- `USER_ANCHORED`: User provided manual GPS → MEDIUM confidence

**Enhanced segment reconnection**:
- When a segment becomes ANCHORED, check for nearby FLOATING segments (within 500m of any anchored position in the new segment)
- Attempt satellite-based position matching between FLOATING segment images and satellite tiles near the ANCHORED segment
- If match found: anchor the floating segment and connect to the trajectory
- DEM consistency check: ensure segment positions are consistent with terrain elevation
- If no match after all frames in floating segment are tried: request user input with configurable timeout (default: 30s), then continue with best VO estimate

### Component: Multi-Provider Satellite Tile Cache

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| Multi-provider progressive cache with DEM | aiohttp, aiofiles, sqlite3, faiss | Multiple providers for coverage. Async download. DINOv2 embeddings pre-computed. DEM cached alongside tiles. | Needs internet. Provider API differences. | Google Maps Tiles API + Mapbox API keys | API keys in env vars only. Never logged. | Async, non-blocking | **Best** |

**Selected**: Multi-provider progressive cache.

**Provider priority**:
1. User-provided tiles (highest priority — custom/recent imagery for the area)
2. Google Maps (zoom 18, ~0.4m/px) — 100K free tiles/month
3. Mapbox Satellite (zoom 16+, up to 0.3m/px) — 200K free requests/month

**Cache strategy**:
1. On job start: download tiles in 1km radius around starting GPS from primary provider
2. Pre-compute SuperPoint features AND DINOv2 embeddings for all cached tiles
3. As route extends: download tiles 500m ahead of estimated position
4. **Request budgeting**: track daily API requests, switch to secondary provider at 80% of daily limit
5. Cache structure on disk:
   ```
   cache/
   ├── tiles/{provider}/{zoom}/{x}/{y}.jpg
   ├── features/{provider}/{zoom}/{x}/{y}_sp.npz     (SuperPoint features)
   ├── embeddings/{provider}/{zoom}/{x}/{y}_dino.npz  (DINOv2 embedding)
   └── dem/{lat}_{lon}.tif                            (Copernicus DEM tiles)
   ```
6. Cache is persistent across jobs — tiles and features reused for overlapping areas
7. **DEM cache**: download Copernicus DEM GLO-30 tiles alongside satellite tiles. 30m resolution is sufficient for terrain correction at flight altitude.

**Tile download budget (revised)**:
- Google Maps: 100,000 tiles/month free → ~50 flights at 2000 tiles each
- Mapbox: 200,000 requests/month free → additional capacity
- Per flight: ~2000 satellite tiles (~80MB) + ~500 DEM tiles (~20MB)
- Combined free tier handles operational volume

### Component: API & Real-Time Streaming

| Solution | Tools | Advantages | Limitations | Requirements | Security | Performance | Fit |
|----------|-------|-----------|-------------|-------------|----------|------------|-----|
| FastAPI + SSE + JWT | FastAPI ≥0.135.0, EventSourceResponse, uvicorn, python-jose | Native SSE. Async pipeline. OpenAPI auto-generated. JWT for proper auth. | Python GIL (mitigated with asyncio + GPU-bound ops). | Python 3.11+, uvicorn | JWT auth, CORS, rate limiting | Async, non-blocking | **Best** |

**Selected**: FastAPI + SSE + JWT authentication.

**API Endpoints**:
```
POST /auth/token
  Body: { api_key }
  Returns: { access_token, token_type, expires_in }

POST /jobs
  Headers: Authorization: Bearer <token>
  Body: { start_lat, start_lon, altitude, camera_params, image_folder }
  Returns: { job_id }

GET /jobs/{job_id}/stream
  Headers: Authorization: Bearer <token>
  SSE stream of:
    - { event: "position", data: { image_id, lat, lon, confidence, segment_id } }
    - { event: "refined", data: { image_id, lat, lon, confidence, delta_m } }
    - { event: "segment_start", data: { segment_id, reason } }
    - { event: "user_input_needed", data: { image_id, reason, timeout_s } }
    - { event: "complete", data: { summary } }

POST /jobs/{job_id}/anchor
  Headers: Authorization: Bearer <token>
  Body: { image_id, lat, lon }
  Manual user GPS input for an image

GET /jobs/{job_id}/point-to-gps?image_id=X&px=100&py=200
  Headers: Authorization: Bearer <token>
  Returns: { lat, lon, confidence }

GET /jobs/{job_id}/results?format=geojson
  Headers: Authorization: Bearer <token>
  Returns: full results as GeoJSON or CSV (WGS84)
```

**Security measures**:
- JWT authentication on all endpoints (short-lived tokens, 1h expiry)
- Image folder whitelist: only paths under configured base directories allowed
- Max image dimensions: 8000×8000 pixels
- Max concurrent SSE connections per client: 5
- Rate limiting: 100 requests/minute per client
- All provider API keys in environment variables, never logged or returned in responses
- CORS configured for known client origins only

### Component: Interactive Point-to-GPS Lookup

For each processed image, the system stores the estimated camera-to-ground homography. Given pixel coordinates (px, py):

1. If image has satellite match: use computed homography to project (px, py) → satellite tile coordinates → WGS84. HIGH confidence.
2. If image has only VO pose: use camera intrinsics + DEM-corrected altitude + estimated heading to ray-cast (px, py) to ground plane → WGS84. MEDIUM confidence.
3. Confidence score derived from underlying position estimate quality.

## Testing Strategy

### Integration / Functional Tests
- End-to-end pipeline test using provided 60-image sample dataset with ground truth GPS
- Verify 80% of positions within 50m of ground truth
- Verify 60% of positions within 20m of ground truth
- Test sharp turn handling: simulate 90° turn with non-overlapping images
- Test segment creation, satellite anchoring, and cross-segment reconnection
- Test user manual anchor injection via POST endpoint
- Test point-to-GPS lookup accuracy against known ground coordinates
- Test SSE streaming delivers results within 1s of processing completion
- Test with FullHD resolution images (pipeline must not fail)
- Test with 6252×4168 images (verify downscaling and memory usage)
- Test DINOv2 coarse retrieval finds correct satellite tile with 100m VO drift
- Test multi-provider fallback: block Google Maps, verify Mapbox takes over
- Test with outdated satellite imagery: verify confidence scores reflect match quality
- Test outlier handling: 350m gap between consecutive photos
- Test image rotation handling: apply 45° rotation to images, verify pipeline handles it

### Non-Functional Tests
- Processing speed: <5s per image on RTX 2060 (target <2s with ONNX optimization)
- Memory: peak RAM <16GB, VRAM <6GB during 3000-image flight at max resolution
- Memory stability: process 3000 images, verify no memory leak (stable RSS over time)
- Concurrent jobs: 2 simultaneous flights, verify isolation and resource sharing
- Tile cache: verify tiles and features are cached and reused across jobs
- API: load test SSE connections (10 simultaneous clients)
- Recovery: kill and restart service mid-job, verify job can resume from last processed image
- DEM download: verify Copernicus DEM tiles fetched and cached correctly
- GTSAM optimizer: verify backward correction produces "refined" events with improved positions

### Security Tests
- JWT authentication enforcement on all endpoints
- Expired/invalid token rejection
- Provider API keys not exposed in responses, logs, or error messages
- Image folder path traversal prevention (attempt to access /etc/passwd via image_folder)
- Image folder whitelist enforcement
- Input validation: invalid GPS coordinates, negative altitude, malformed camera params
- Rate limiting: verify 429 response after exceeding limit
- Max SSE connection enforcement
- Max image size validation (reject >8000px)
- CORS enforcement: reject requests from unknown origins

## References
- [YFS90/GNSS-Denied-UAV-Geolocalization](https://github.com/YFS90/GNSS-Denied-UAV-Geolocalization) — <7m MAE with terrain-weighted constraint optimization
- [SatLoc-Fusion (2025)](https://www.mdpi.com/2072-4292/17/17/3048) — hierarchical DINOv2+XFeat+optical flow, <15m on edge hardware
- [CEUSP (2025)](https://arxiv.org/abs/2502.11408) — DINOv2-based cross-view UAV self-positioning
- [Oblique-Robust AVL (IEEE TGRS 2024)](https://ieeexplore.ieee.org/iel7/36/10354519/10356107.pdf) — rotation-equivariant features for UAV-satellite matching
- [XFeat (CVPR 2024)](https://github.com/verlab/accelerated_features) — 5x faster than SuperPoint
- [LightGlue-ONNX](https://github.com/fabio-sim/LightGlue-ONNX) — 2-4x speedup via ONNX/TensorRT
- [DALGlue (2025)](https://www.nature.com/articles/s41598-025-21602-5) — 11.8% MMA improvement over LightGlue for UAV
- [SIFT+LightGlue UAV Mosaicking (ISPRS 2025)](https://isprs-archives.copernicus.org/articles/XLVIII-2-W11-2025/169/2025/) — SIFT superior for high-rotation conditions
- [LightGlue rotation issue #64](https://github.com/cvg/LightGlue/issues/64) — confirmed not rotation-invariant
- [LightGlue no-match issue #13](https://github.com/cvg/LightGlue/issues/13) — false matches on non-overlapping pairs
- [GTSAM v4.2](https://github.com/borglab/gtsam) — factor graph optimization with Python bindings
- [Copernicus DEM GLO-30](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM) — free 30m global DEM
- [Google Maps Tiles API](https://developers.google.com/maps/documentation/tile/satellite) — satellite tiles, 100K free/month
- [Mapbox Satellite](https://docs.mapbox.com/data/tilesets/reference/mapbox-satellite/) — alternative tile provider, up to 0.3m/px
- [DINOv2 UAV Self-Localization (2025)](https://ui.adsabs.harvard.edu/abs/2025IRAL...10.2080Y/) — 86.27 R@1 on DenseUAV
- [FastAPI SSE](https://fastapi.tiangolo.com/tutorial/server-sent-events/)
- [Homography Decomposition Revisited (IJCV 2025)](https://link.springer.com/article/10.1007/s11263-025-02680-4)
- [Sliding Window Factor Graph Optimization (2020)](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/523C7C41D18A8D7C159C59235DF502D0/)

## Related Artifacts
- Assessment research: `_docs/00_research/gps_denied_nav_assessment/`
- Previous AC assessment: `_docs/00_research/gps_denied_visual_nav/00_ac_assessment.md`
- Previous comparison framework: `_docs/00_research/gps_denied_visual_nav/03_comparison_framework.md`
