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https://github.com/azaion/gps-denied-onboard.git
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Oleksandr Bezdieniezhnykh
9eba1689b3
- Revised acceptance criteria in the acceptance_criteria.md file to clarify metrics and expectations, including updates to GPS accuracy and image processing quality. - Enhanced restrictions documentation to reflect operational parameters and constraints for UAV flights, including camera specifications and satellite imagery usage. - Added new research documents for acceptance criteria assessment and question decomposition to support ongoing project evaluation and decision-making.
104 lines
26 KiB
Markdown
104 lines
26 KiB
Markdown
# Source Registry — Phase 1 (AC & Restrictions Assessment)
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Tier legend: L1 = official spec / standard / reference manual; L2 = peer-reviewed paper or tool from a vendor / SOTA author; L3 = vendor docs, popular OSS repo, expert blog; L4 = forum post, secondary blog.
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| ID | Tier | Title | URL | Used for |
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| S01 | L2 | Xu et al., *UAV-VisLoc: A Large-scale Dataset for UAV Visual Localization* (arXiv 2405.11936, May 2024) | https://arxiv.org/html/2405.11936v1 | Fixed-wing UAV visual localization benchmark; 405–840 m altitudes; 0.3 m/px Google Earth satellite reference |
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| S02 | L2 | Xu et al., *Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation Condition: a Benchmark* (AnyVisLoc, arXiv 2503.10692, 2025) | https://arxiv.org/html/2503.10692v1 | SOTA recall@Xm numbers; 74.1% @ 5 m at 30–300 m altitude |
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| S03 | L2 | He et al., *AerialVL: A Dataset, Baseline and Algorithm Framework for Aerial-Based Visual Localization With Reference Map* (RA-L 2024) | https://ieeexplore.ieee.org/document/10632587 ; https://github.com/hmf21/AerialVL | Fixed-wing aerial VPR + visual alignment + VO benchmark; 70 km of trajectories; FLIR + gimbal + NovAtel GNSS 1.5 m RMS |
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| S04 | L2 | Schmidt-Salzmann et al., *Visual Place Recognition for Aerial Imagery: A Survey* (arXiv 2406.00885, 2024) + aero-vloc benchmark | https://arxiv.org/abs/2406.00885 ; https://github.com/prime-slam/aero-vloc | VPR methods (AnyLoc, CosPlace, EigenPlaces, MixVPR, NetVLAD, SALAD, SelaVPR) for aerial domain |
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| S05 | L2 | Keetha et al., *AnyLoc: Towards Universal Visual Place Recognition* | https://anyloc.github.io/ ; https://github.com/AnyLoc/AnyLoc | DINOv2 + VLAD VPR, training-free, strong on aerial cross-domain |
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| S06 | L2 | Ali-bey et al., *MixVPR: Feature Mixing for Visual Place Recognition* (arXiv 2303.02190) | https://arxiv.org/abs/2303.02190 | Lightweight VPR aggregation, 94.6% R@1 Pitts250k |
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| S07 | L2 | Lindenberger et al., *LightGlue: Local Feature Matching at Light Speed* | https://github.com/cvg/LightGlue | Real-time matcher (with SuperPoint) |
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| S08 | L2 | Potje et al., *XFeat: Accelerated Features for Lightweight Image Matching* (CVPR 2024) | https://openaccess.thecvf.com/content/CVPR2024/papers/Potje_XFeat_Accelerated_Features_for_Lightweight_Image_Matching_CVPR_2024_paper.pdf ; https://github.com/verlab/accelerated_features | 5× faster than LightGlue, designed for embedded; semi-dense option |
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| S09 | L2 | Leroy et al., *Grounding Image Matching in 3D with MASt3R* (ECCV 2024) | https://arxiv.org/abs/2406.09756 | Cross-view 3D-grounded matching; +30% AUC on Map-free |
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| S10 | L3 | `fettahyildizz/superpoint_lightglue_tensorrt` (TRT 8.5.2.2, dynamic shapes) | https://github.com/fettahyildizz/superpoint_lightglue_tensorrt | TensorRT-ready SuperPoint+LightGlue C++ |
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| S11 | L3 | `yuefanhao/SuperPoint-LightGlue-TensorRT` | https://github.com/yuefanhao/SuperPoint-LightGlue-TensorRT | RTX3080 baseline: SP 0.95 ms + LG 2.54 ms @ 320×240 = 286 FPS |
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| S12 | L3 | `qdLMF/LightGlue-with-FlashAttentionV2-TensorRT` (Jetson Orin NX, CUTLASS plugin) | https://github.com/qdLMF/LightGlue-with-FlashAttentionV2-TensorRT | Jetson Orin–class deployment proof |
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| S13 | L3 | `fabio-sim/LightGlue-ONNX` (FP8) | https://github.com/fabio-sim/LightGlue-ONNX | ONNX/TRT path for matchers |
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| S14 | L1 | NVIDIA — *JetPack 6.2 brings Super Mode to Jetson Orin Nano and Orin NX* | https://developer.nvidia.com/blog/nvidia-jetpack-6-2-brings-super-mode-to-nvidia-jetson-orin-nano-and-jetson-orin-nx-modules/ | Confirms 67 TOPS sparse INT8, 15/25 W/MAXN SUPER modes, 8 GB shared LPDDR5 |
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| S15 | L1 | NVIDIA — *Jetson Orin Nano / Orin NX / AGX Orin Power & Performance* | https://docs.nvidia.com/jetson/archives/r35.6.1/DeveloperGuide/SD/PlatformPowerAndPerformance/ | Power-mode specifics, throttling behaviour |
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| S16 | L1 | ArduPilot — *MAVProxy GPSInput module* | https://ardupilot.org/mavproxy/docs/modules/GPSInput.html | GPS1_TYPE=14 (MAVLink); GPS_INPUT message fields |
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| S17 | L1 | ArduPilot — *MAVProxy GPSInput source* | https://github.com/ArduPilot/MAVProxy/blob/master/MAVProxy/modules/mavproxy_GPSInput.py | Reference impl for GPS_INPUT injection |
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| S18 | L3 | mavlink/MAVSDK-Python issue #320 — *Input external gps through mavsdk* | https://github.com/mavlink/MAVSDK-Python/issues/320 | MAVSDK has no native GPS_INPUT support — must use pymavlink |
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| S19 | L1 | PX4 PR #21244, #23366 — *Add GPS spoofing state* / *EKF2 spoofing GPS check* | https://github.com/PX4/PX4-Autopilot/pull/21244 ; https://github.com/PX4/PX4-Autopilot/pull/23366 | PX4 spoofing flag, ~1 s hysteresis; EKF2 disables GNSS fusion when spoofed |
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| S20 | L1 | PX4 PR #23346 — *EKF2 fix timeout after gps failure* | https://github.com/PX4/PX4-Autopilot/pull/23346 | Dead-reckoning timeout logic |
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| S21 | L1 | PX4 issue #23970 — *COM_POS_FS_DELAY does not take effect* | https://github.com/PX4/PX4-Autopilot/issues/23970 | Failsafe delay parameter behaviour (default 1 s) |
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| S22 | L1 | Google — *Map Tiles API Policies* | https://developers.google.com/maps/documentation/tile/policies | Explicit prohibition: "Offline uses … Image analysis, Machine interpretation, Object detection or identification, Geodata extraction or resale" |
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| S23 | L1 | Google — *Maps Platform Terms of Service* | https://developers.google.com/maps/terms | Prohibits use "with any products, systems, or applications for … any systems or functions for automatic or autonomous control of vehicle behavior" |
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| S24 | L1 | Microsoft — *Bing Maps Terms of Use (April 2024)* | https://www.bingmapsportal.com/terms/TermsApril2024 | Bing tiles cannot be cached/stored offline; tile URLs are not stable |
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| S25 | L1 | Maxar/Vantor — *Vivid Mosaic 30 cm Basemaps* | https://maxar.com/precision ; https://developers.maxar.com/docs/ordering/guides/vivid-standard-30 | 30 cm global mosaic (135 M km²), 15 cm urban mosaic (7 M km²), AI change detection refresh; ~$25–32/km² archive |
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| S26 | L1 | Airbus — *Order Pléiades Neo (30 cm)* | https://space-solutions.airbus.com/imagery/how-to-order-imagery-and-data/how-to-order-pleiades-neo/ | Pléiades Neo 30 cm, OneAtlas tasking; ~€5–8.50/km² volume tier |
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| S27 | L1 | Planet Community — *Commercial imagery pricing* | https://community.planet.com/advanced-analysis-apis-81/commercial-imagery-pricing-4926 | SkySat / PlanetScope pricing tiers |
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| S28 | L3 | EOX — *Sentinel-2 cloudless (s2maps.eu)* | https://s2maps.com/ | Free 10 m/px global mosaic; updated annually; CC-BY-NC for non-commercial |
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| S29 | L3 | UAV Coach — *GSD calculator* | https://uavcoach.com/gsd-calculator/ | GSD = (alt × sensor_w) / (focal × image_w); validates ~24 cm/px at 1 km AGL with full-frame 24 mm |
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| S30 | L2 | Mid-Air dataset (synthetic, quadcopter, IMU + GPS + 420k frames) | https://midair.ulg.ac.be/ | Training-time augmentation candidate (synthetic) |
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| S31 | L2 | AgriLiRa4D (LiDAR + 4D radar + IMU, 5–18 m AGL agriculture) | https://arxiv.org/html/2512.01753v1 | Out of altitude band — only useful for SLAM regression baselines |
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| S32 | L2 | Survey & comparison of ORB-SLAM3 / VINS-Fusion / DROID-SLAM / RTAB-Map | https://article.isarpublisher.com/viewArticle/Numerical-Evaluation-and-Comparative-Analysis-of-Visual-Inertial-SLAM-Algorithms-ORB-SLAM3-VINS-Fusion-DROID-SLAM-and-RTAB-Map | VIO drift baselines |
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| S33 | L3 | nicholasaleks/Damn-Vulnerable-Drone wiki — *GPS Data Injection* | https://github.com/nicholasaleks/Damn-Vulnerable-Drone/wiki/GPS-Data-Injection | Confirms ArduPilot blends GPS sources by quality; security implications of GPS_INPUT |
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| S34 | L1 | QGroundControl — *StatusTextHandler / RequestMessageState API* | https://api.qgroundcontrol.com/master/classStatusTextHandler.html | STATUSTEXT pipeline used for companion-computer comms |
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| S35 | L4 | mavlink/qgroundcontrol issue #7599 — *Display Companion Status on QGC* | https://github.com/mavlink/qgroundcontrol/issues/7599 | Companion-computer status display gap; ONBOARD_COMPUTER_STATUS workflow |
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| S36 | L2 | Bian et al., *ViewBridge: Revisiting Cross-View Localization from Image Matching* (arXiv 2508.10716, 2025) | https://arxiv.org/abs/2508.10716 | CVFM benchmark, 32,509 cross-view pairs, BEV projection + similarity refinement |
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| S37 | L2 | OrthoLoC (2025) — UAV-to-orthographic 6-DoF localization with AdHoP refinement | (referenced in cross-view SOTA results) | Compatible with any matcher; ↑95% match quality, ↓63% translation error |
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| S38 | L3 | LAND INFO — *Satellite imagery pricing* | https://www.landinfo.com/satellite-imagery-pricing.html | Cross-vendor reference pricing (WV-3/4 30 cm pansharpened: $25.50–32.50/km² archive vs new) |
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| S39 | L2 | Cross-view UAV-satellite matching survey (MDPI Sensors 2024) | https://www.mdpi.com/1424-8220/24/12/3719 | RDS 84.40%, MA@20 83.35% — practical accuracy ceiling for cross-view in mostly-nadir setup |
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**Coverage notes**
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- Multiple L1/L2 sources for every quantitative AC line (accuracy, MRE, latency, hardware envelope, tile size).
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- The Google Maps + Bing Maps offline-prohibition findings have **two L1 sources each** (terms of service + dev-platform AUP).
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- The "fixed-wing 1 km AGL with public IMU" gap is a **finding**, not a fixable source — no public dataset matches all four constraints simultaneously.
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---
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## Mode B (Solution Assessment) sources — appended 2026-04-26
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| ID | Tier | Title | URL | Used for |
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| S40 | L1 | NVIDIA Jetson AI Lab — *Benchmarks (DINOv2-base-patch14, ViT-base, CLIP-ViT-base)* | https://www.jetson-ai-lab.com/archive/benchmarks.html | Measured Orin Nano Super throughput: DINOv2-base-patch14 = **126 inf/s** (Super), 75 inf/s (original); CLIP-ViT-base/16 = 161 inf/s; ViT-base/16 = 158 inf/s. Real numbers for AnyLoc backbone (W2.a / W9.a). |
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| S41 | L1 | ArduPilot — *Non-GPS Position Estimation* (dev docs) | https://ardupilot.org/dev/docs/mavlink-nongps-position-estimation.html | **ODOMETRY is the preferred external-nav method** in ArduPilot (over VISION_POSITION_ESTIMATE and over GPS_INPUT for non-GPS-substitute use). Carries quaternion, velocity, **21-element pos+attitude covariance**, and a `quality` field (-1=failed → 100=best). VISO_QUAL_MIN gates ignored messages. |
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| S42 | L1 | ArduPilot PR #19563 — *VisualOdom: Support ODOMETRY mavlink message* | https://github.com/ArduPilot/ardupilot/pull/19563 | ODOMETRY support landed Dec 2021 for the Plane stack as well as Copter; tested with ModalAI VOXL VIO. |
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| S43 | L1 | ArduPilot PR #30080 — *External nav+gps fix* | https://github.com/ArduPilot/ardupilot/pull/30080 | Active 2025 work on source-switching when running external nav alongside GPS — confirms there are real edge cases when migrating between GPS_INPUT and ODOMETRY mid-flight. Relevant to AC-NEW-2 (spoofing-promotion latency). |
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| S44 | L1 | ArduPilot Plane — *MAVLink2 Signing* | https://ardupilot.org/plane/docs/common-MAVLink2-signing.html | Signing is per-link, USB bypasses signing, keys live in FRAM (32-byte secret + timestamp). Configured via Mission Planner. Production-mature in ArduPilot 4.5+ but key-distribution is an operator step. |
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| S45 | L3 | mavlink-router issue #436 — *Stack-based buffer overflow in ConfFile::get_sections* | https://github.com/mavlink-router/mavlink-router/issues/436 | Public, easily-triggered overflow in config-file parsing of mavlink-router. Repo has **no formal security policy / no SECURITY.md**. Direct attack surface for any project that uses mavlink-router on the companion. |
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| S46 | L2 | Ali-bey et al., *BoQ: A Place is Worth a Bag of Learnable Queries* (CVPR 2024) | https://arxiv.org/abs/2405.07364 ; https://github.com/amaralibey/bag-of-queries | New VPR SOTA (CVPR 2024); cross-attention over learnable queries; works on CNN + ViT backbones; **outperforms NetVLAD, MixVPR, EigenPlaces** + outperforms two-stage (Patch-NetVLAD, TransVPR, R2Former) at lower cost. DinoV2 results added Nov 2024. |
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| S47 | L2 | Izquierdo & Civera, *DINOv2 SALAD: Optimal Transport Aggregation for VPR* (CVPR 2024) | https://serizba.github.io/salad.html ; https://github.com/serizba/salad | DINOv2 + Sinkhorn-based optimal-transport VLAD aggregation; **R@1 75% on MSLS Challenge, 92.2% on MSLS Val, 76% on NordLand**. Already in `aero-vloc` benchmark, so we get an apples-to-apples bench against AnyLoc/MixVPR/EigenPlaces. |
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| S48 | L2 | Shen et al., *GIM: Learning Generalizable Image Matcher From Internet Videos* (ICLR 2024 spotlight) | https://arxiv.org/abs/2402.11095 ; https://github.com/xuelunshen/gim ; https://xuelunshen.com/gim | Self-training on 50 h of YouTube videos → **8.4–18.1% relative zero-shot improvement** over LightGlue / RoMa / DKM / LoFTR baselines. ZEB benchmark (zero-shot evaluation). Same architecture, more general training. |
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| S49 | L2 | *AerialExtreMatch: A Benchmark for Extreme-View Image Matching and UAV Localization* | https://openreview.net/forum?id=5a5T3IW2B6 | 1.5 M synthetic image-pair benchmark with **32 difficulty levels** (overlap × scale × pitch). Real-world UAV localization subset. Direct measurement of the failure-mode that worries us most. |
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| S50 | L2 | *2chADCNN: Template Matching for Season-Changing UAV Aerial Images and Satellite Imagery* (MDPI Drones 2023) | https://www.mdpi.com/2504-446X/7/9/558 | Two-channel CNN trained for cross-season UAV↔satellite matching. Useful both as season-robustness baseline and as a target for the bench-off (does the SOTA matcher really need season-aware training, or do generic GIM/RoMa already win?). |
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| S51 | L2 | TartanAir V2 — photorealistic synthetic SLAM dataset | https://tartanair.org/ ; https://tartanair.org/modalities.html | 65 environments, 12-camera rig, IMU + LiDAR + depth + semantic + flow + event modalities, custom camera models (pinhole / fisheye / equirectangular). Photorealistic (AirSim-based). Higher fidelity than MidAir. |
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| S52 | L2 | Kim — *Monocular Visual Odometry for Fixed-Wing Small Unmanned Aircraft Systems* (AFIT thesis #2266) | https://scholar.afit.edu/etd/2266 | SOTA monocular VO (SVO, DSO, ORB-SLAM2) tested on real fixed-wing flights — **all three had significant difficulty maintaining localisation**. Confirms VO-only is not viable; the draft's "VO between satellite anchors" framing is the right answer. |
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| S53 | L2 | Quan & Cao, *Visual-Inertial Odometry Using High Flying Altitude Drone Datasets* (MDPI Drones 2023) | https://www.mdpi.com/2504-446X/7/1/36 | High-altitude VIO performance numbers for the 300–1000 m AGL band — directly applicable to our 1 km AGL operating band; benchmark baseline for AC-1.3. |
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| S54 | L1 | mapproxy issue #196 + maplibre/martin `mbtiles` pool | https://github.com/mapproxy/mapproxy/issues/196 ; https://github.com/maplibre/martin/blob/738c55e9/mbtiles/src/pool.rs | Operational recipe for MBTiles SQLite under concurrent read+write: **WAL mode + connection pool + transaction batching**. Non-WAL MBTiles is the typical reason "MBTiles is slow" complaints exist. |
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| S55 | L1 | Python.org — *Free-threaded mode (Python 3.13)* | https://docs.python.org/3.13/howto/free-threading-python.html ; https://py-free-threading.github.io/ | Free-threading is **experimental** in 3.13; has "substantial single-threaded performance hit"; many C extensions don't support it; GIL auto-re-enables on import of non-FT-aware extensions. Not v1-ready. |
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| S56 | L2 | Lazarski et al. — *Terrain Analysis in Eastern Ukraine* (Kharkiv-region UAV survey, IEEE 2018) | https://ieeexplore.ieee.org/document/8441556 ; http://www.50northspatial.org/medium-cost-uav-mapping/ | **Eastern-Ukraine relief amplitude ≈ 24 m peak-to-trough** in Kharkiv test areas, with creek + gully (yary) systems. Quantifies the residual error of the flat-Earth ortho assumption (R-Terrain). |
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| S57 | L1 | aedelon/mast3r-runtime | https://github.com/aedelon/mast3r-runtime | MASt3R inference runtime: **Jetson Orin support listed as "Planned"**, not implemented. Plus *Speedy MASt3R* paper achieves 91 ms/pair on **A40 GPU** — Jetson Orin Nano Super is roughly 1/30 of A40 throughput, putting MASt3R at ~3 s/pair on our target hardware. |
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---
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## Mode B Round 2 (component-replacement deep-dive) — appended 2026-04-26
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| ID | Tier | Title | URL | Used for |
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| S58 | L2 | Yang et al., *LiteSAM: Lightweight and Robust Feature Matching for Satellite and Aerial Imagery* (Remote Sensing 17(19):3349, MDPI, Oct 2025) | https://www.mdpi.com/2072-4292/17/19/3349 ; https://github.com/boyagesmile/LiteSAM | Purpose-built satellite↔aerial matcher. **6.31 M params (2.4× smaller than EfficientLoFTR's 15.05 M); RMSE@30 = 17.86 m on UAV-VisLoc (beats EfficientLoFTR); 61.98 ms / pair on standard GPU; 497.49 ms / pair on Jetson AGX Orin (= 22.9% / 19.8% faster than EfficientLoFTR-optimized).** Components: TAIFormer (token-aggregation transformer with conv token mixer) + MinGRU dynamic sub-pixel refinement. |
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| S59 | L1 | leftfield-geospatial/orthority — Python orthorectification toolkit | https://orthority.readthedocs.io/ ; https://github.com/leftfield-geospatial/orthority ; https://pypi.org/project/orthority/ | **Per-image orthorectification** as a Python library (frame + RPC camera models, GeoTIFF DEM, RPC refinement, pan-sharpening). Successor of `dugalh/simple-ortho`. Pip/conda installable; CLI + API. Direct fit for Component 1b's per-frame ortho step (replaces hand-rolled pinhole-on-DEM code). |
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| S60 | L2 | Korovko et al. (NVIDIA), *cuVSLAM: CUDA-Accelerated Visual Odometry and Mapping* (arXiv 2506.04359, Jul 2025) | https://arxiv.org/abs/2506.04359 ; https://github.com/nvidia-isaac/cuVSLAM ; https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_visual_slam | NVIDIA's CUDA-accelerated VSLAM, **explicitly optimized for Jetson edge devices**. Modular front-end (Shi-Tomasi GFTT keypoints + LK pyramidal tracking + NCC consistency check) and back-end (sparse bundle adjustment + pose-graph optimization + loop closure). Supports 1 → 32 cameras, monocular + monocular-depth + stereo + multi-stereo, optional IMU. **<1 % ATE on KITTI; <5 cm on EuRoC**, real-time on Orin platforms. Apache-2.0. Drop-in via `isaac_ros_visual_slam` ROS 2 package. |
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| S61 | L2 | Liao, *DPVO-QAT++: Heterogeneous QAT and CUDA Kernel Fusion for High-Performance Deep Patch Visual Odometry* (arXiv 2511.12653, Nov 2025) | https://arxiv.org/abs/2511.12653 ; https://arxiv.org/html/2511.12653v1 | Quantization-aware training + CUDA kernel fusion for DPVO front-end (back-end stays FP32). On RTX-4060: **+52% FPS (TartanAir), +30% FPS (EuRoC), −37–65 % peak GPU memory**, ATE preserved. Confirms the "deployment gap" framing: **even DPVO-QAT++ is benchmarked on RTX-4060, NOT on Jetson** — Orin Nano Super extrapolation puts plain DPVO at ≈4–10 FPS (well under our 10 Hz inference target). |
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| S62 | L2 | Murai et al. (Imperial / NVIDIA), *MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors* (CVPR 2025) | https://arxiv.org/abs/2412.12392 ; https://github.com/rmurai0610/MASt3R-SLAM ; https://opencv.org/mast3r-slam/ | Dense monocular SLAM built on MASt3R prior. **15 FPS on a single GPU**; outperforms DROID-SLAM on EuRoC + 7-Scenes; calibration-free. **No Jetson port**; given Speedy MASt3R = 91 ms/pair on A40, MASt3R-SLAM on Orin Nano Super is sub-1-Hz → **infeasible for inline v1 use**. Useful as offline ground-truth oracle or future-track candidate. |
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| S63 | L2 | Edstedt et al., *RoMa v2: Harder Better Faster Denser Feature Matching* (arXiv 2511.15706, Nov 2025) | https://arxiv.org/abs/2511.15706 ; https://github.com/Parskatt/romav2 | New SOTA dense matcher: frozen DINOv3 backbone + custom CUDA + predictive covariance + decoupled match-then-refine. Best published pose-estimation accuracy. **Compute footprint is GPU-class**; not a candidate for inline Jetson Orin Nano Super inference, but a plausible offline ceiling reference for the Component-3 bench-off. |
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| S64 | L1 | NVIDIA Isaac ROS — *Visual SLAM* (Jetson tutorial + reference implementation) | https://nvidia-ai-iot.github.io/jetson_isaac_ros_visual_slam_tutorial/ ; https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_visual_slam ; https://github.com/bandofpv/VSLAM-UAV | **Reference implementation of GPS-denied UAV with cuVSLAM on Jetson Orin Nano + RealSense D435i + MAVROS + PX4** (Hackster.io / bandofpv). Demonstrates the production-deployable path: cuVSLAM publishes ROS 2 pose; MAVROS converts to MAVLink; FC consumes via VISION_POSITION_ESTIMATE / ODOMETRY. ArduPilot variant exists (sidharthmohannair/ros2-ardupilot-sitl-hardware). |
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| S65 | L1 | ArduPilot issue #30076 — *Fixing ExternalNav + GPS* | https://github.com/ArduPilot/ardupilot/issues/30076 | **EKF3 incorrectly fuses GPS data simultaneously when ExtNav is the configured POSXY source** — root cause was a stray `else` branch in `FuseVelPosNED()`. Causes "unstable positions with high variances and reset behavior when position estimates diverge". Documents the **double-fusion-is-not-a-feature** invariant for our hybrid `GPS_INPUT + ODOMETRY` plan. Status: PR landed; pin ArduPilot to a fixed version. |
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| S66 | L1 | ArduPilot issue #32506 — *EKF3 Position Down snaps to ODOMETRY Z value when ExternalNav is not configured as POSZ source* | https://github.com/ArduPilot/ardupilot/issues/32506 | Sister bug to #30076: Z-axis snap-to-ODOMETRY when only POSXY uses ExtNav. Reinforces the "**only one horizontal position source active at a time**" architectural invariant — feeding both GPS_INPUT and ODOMETRY for the same axis is a configuration error, not a feature. Has a direct impact on draft02's M-1 conclusion. |
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| S67 | L1 | ArduPilot wiki — *EKF Sources* (`common-ekf-sources.rst`) | https://github.com/ArduPilot/ardupilot_wiki/blob/master/common/source/docs/common-ekf-sources.rst | Authoritative spec for `EK3_SRC1_*` / `EK3_SRC2_*` / `EK3_SRC3_*` and runtime source switching via RC aux or MAVLink. Confirms architectural rule: **only one position source per axis at a time**; ExtNav is option 6. |
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| S68 | L1 | PX4 PR #22262 — *EKF2: Error-State Kalman Filter* | https://github.com/PX4/PX4-Autopilot/pull/22262 | Confirms PX4 EKF2 is an **ESKF** (in contrast to ArduPilot's EKF3 which is a classical extended Kalman filter). Real-hardware PX4 testing: ESKF reduces CPU load by ~0.3 % vs total-state EKF on autopilot. Key takeaway: **ArduPilot users (us) cannot swap the FC filter to ESKF** — the FC-side debate is moot. ESKF only matters for any companion-side filter we choose to add. |
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| S69 | L2 | Sola, *Quaternion kinematics for the error-state Kalman filter* (arXiv 1711.02508) + Madgwick / Solà / Forster references | https://arxiv.org/abs/1711.02508 | Canonical ESKF treatment: nominal + error-state decomposition, tangent-space covariance, retraction through `Exp/Log` on SO(3) / SE(3). The standard reference for any companion-side ESKF implementation. |
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| S70 | L2 | Yu et al., *T-ESKF: Transformed Error-State Kalman Filter for Consistent Visual-Inertial Navigation* (arXiv 2510.23359, Oct 2025) + *Adaptive Covariance and Quaternion-Focused Hybrid ESKF/UKF for VIO* (arXiv 2512.17505, Dec 2025) | https://arxiv.org/abs/2510.23359 ; https://arxiv.org/abs/2512.17505 | 2025 advances on top of ESKF: T-ESKF restores observability consistency under partial-yaw observability; Hybrid ESKF/UKF gains **+49 % position / +57 % rotation accuracy vs pure ESKF, ~48 % cheaper than full SUKF**. Both are research-track; v1 if we run a companion-side filter at all, vanilla ESKF is enough. |
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| S71 | L1 | OpenStreetMap Sensors / VINS-Fusion + OpenVINS Jetson Orin Nano integration reports | https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/issues/220 ; https://github.com/rpng/open_vins/issues/421 ; https://github.com/fdcl-gwu/openvins_jetson_realsense | Field reports: VINS-Fusion runs ~15 FPS on Xavier NX after OpenCV pinning; on Orin Nano builds with JetPack 6 + ROS 2 Humble after fixing OpenCV ArUco/CUDA mismatches. Useful as **comparison baselines for any cuVSLAM bench-off, not as primary candidates** (integration cost dwarfs cuVSLAM's drop-in). |
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| S72 | L2 | Quan et al., *Visual-Inertial Odometry Using High Flying Altitude Drone Datasets* (Drones 7(1):36, MDPI 2023) | https://www.mdpi.com/2504-446X/7/1/36 | High-altitude (40–100 m) VIO field tests: **stereo-VIO = 2.186 m error over 800 m trajectory; monocular VIO "acceptable but worse than stereo"**. Lower bound on the altitude band; our regime is 1 km AGL where motion-parallax VO degrades further (most VO benchmarks assume non-trivial parallax per frame). Reinforces R8. |
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| S73 | L2 | Princeton VL — *Deep Patch Visual SLAM* (DPV-SLAM, ECCV 2024) | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00272.pdf ; https://github.com/iis-esslingen/DPV-SLAM | DPV-SLAM = DPVO + two loop-closure mechanisms. **2.5× faster than DROID-SLAM on EuRoC, 5–7 GB GPU memory vs DROID's 24 GB**, 1×–4× real-time on real-world datasets. Same Jetson-deployment caveat as DPVO. |
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| S74 | L2 | OrthoLoC + AdHoP — UAV-to-orthographic 6-DoF localization with feature-matcher refinement | (referenced in cross-view SOTA results) | Compatible with **any matcher** (drop-in refinement layer): up to **+95 % matching accuracy / −63 % translation error**. Architecturally orthogonal to the matcher choice itself; we can layer this on top of SP+LG / GIM-LG / LiteSAM regardless of which wins the bench-off. |
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| S75 | L2 | AerialExtreMatch open-review (1.5 M synthetic pairs, 32 difficulty levels) — methods evaluated table | https://openreview.net/forum?id=5a5T3IW2B6 ; https://github.com/Xecades/AerialExtreMatch | Confirms AerialExtreMatch evaluates **16 representative matchers** (detector-based + detector-free), with publicly-available results. Becomes our primary structured-difficulty regression bench (already in draft02 as F-T5b). |
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| S76 | L4 | Stack Overflow / Jetson dev forum — *Orin Nano FP16/INT8 throughput discussion* | https://forums.developer.nvidia.com/t/jetson-orin-nano-fp16-int8-performance/326723 ; https://github.com/ultralytics/ultralytics (YOLO26 Jetson Orin Nano Super benchmark commit 8d4e6e8) | Empirical reference points on Orin Nano Super: **FP16 ≈ 4.5 ms / INT8 ≈ 3.8 ms per YOLO26-n inference**. Useful sanity-check rate: small TRT engines run in single-digit ms; SP+LG / GIM-LG family fits comfortably in our budget. |
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| S77 | L2 | thomasthelliez.com — *ROS 2 / Isaac ROS on Jetson Orin Nano Super practical guide* + Hackster.io GPS-Denied Drone reference design | https://thomasthelliez.com/blog/isaac-ros-on-nvidia-jetson-orin-nano-super/ ; https://www.hackster.io/bandofpv/gps-denied-drone-with-nvidia-jetson-orin-nano-9f3417 | **ROS 2 Humble + JetPack 6 + Isaac ROS 3.2 + cuVSLAM + MAVROS** is a working reference architecture on the exact target hardware (Orin Nano Super). Establishes ROS 2 vs DIY Python orchestrator as a real alternative for Component 9. |
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