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Oleksandr Bezdieniezhnykh 9eba1689b3 - Introduced a new document detailing the current state of the autodev process, including steps, status, and findings. 
- 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.
2026-04-26 14:28:10 +03:00

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Mode B — Round 2 Question Decomposition

Trigger: user explicit ask after rolling back from Step 3 (Plan).

Mode: B (Solution Assessment of solution_draft02.md).

Date: 2026-04-26.

Scope (user-provided):

"1. For VO — is it the most efficient method SP+LG for jetson? are there better ways? 2. for cross-view matcher — there is LiteSAM (https://github.com/boyagesmile/LiteSAM) and other methods specialized for that. Check and investigate in internet possible options. 3. EKF fusion — isn't it ESKF better? Ortho-tile generator — are there are already existing libs for that? or it is not so difficult and easier just to make it manually by ourselves? All in all, make a thorough investigation regarding each component — what's could be either give better confidence with relatively same resource and time footprint, either can provide roughly same confidence faster or lighter on resources."

Question Type Classification

# Sub-Question Type Why
Q-R2-1 Is the SP+LG-based VO design (custom 2-frame homography) the most efficient & accurate VO on Orin Nano Super, or is there a better one? Decision Support + Problem Diagnosis Trade-off (compute vs accuracy vs maturity) + diagnoses whether the draft02 design choice is sound.
Q-R2-2 Should LiteSAM (or any specialized satellite-aerial matcher) replace SP+LG / GIM-LG as the inline cross-view matcher? Decision Support Trade-off (accuracy vs latency vs role-fit).
Q-R2-3 Is ESKF strictly better than EKF for our fusion stage? Decision Support + Concept Comparison Comparison + applicability boundary (ArduPilot vs companion).
Q-R2-4 Should we use an existing ortho-tile generator library, or DIY? Decision Support Build-vs-buy.
Q-R2-5 Is there a newer/better option for every other component (VPR, tile storage, MAVLink, software platform, DEM, etc.) that could give better confidence at same/lower resource footprint? Knowledge Organization + Decision Support Sweep audit of remaining components.

Mode-B classification rule: Problem Diagnosis + Decision Support — applies to every sub-question above.

Research Subject Boundary Definition

Dimension Boundary Notes
Population Embedded autonomous-flight stack on Jetson Orin Nano Super (8 GB shared) companion + ArduPilot 4.5+ flight controller. Fixed-wing UAV airframe, 1 km AGL nadir nav cam, ADTi 20MP APS-C @ 3 fps. Same as round 1.
Geography Eastern-Ukraine theatre (active conflict, season variation). Same as round 1.
Timeframe v1 release 2026; v1.1 within 6 months. Same as round 1.
Level Software architecture and component selection (no hardware / no airframe / no GCS). Same as round 1.

Perspectives Used (≥3 required)

Perspective Why this round Example searches
Implementer / Engineer Round 1 missed a few real engineering gotchas (companion-side filter double-fusion bugs, cuVSLAM as drop-in alternative). "ArduPilot ExtNav GPS_INPUT double fusion", "cuVSLAM Jetson Orin Nano monocular fixed-wing"
Practitioner / Field Look at production GPS-denied UAV reference designs on the same hardware target. "ROS 2 Humble Jetson Orin Nano Super JetPack 6 MAVROS ArduPilot integration GPS-denied", "VINS-Fusion OpenVINS BASALT SVO Pro Jetson Orin Nano benchmark monocular fixed-wing 2025"
Domain expert / Academic Verify SOTA matcher and SLAM landscape post-Mode-A. "MASt3R-SLAM monocular real-time 2025 Jetson DROID-SLAM MAC-VO", "RoMa DKM dense feature matching aerial satellite UAV-VisLoc 2025"
Contrarian Actively search for "why not the chosen approach": custom 2-frame VO, SP+LG-only matcher, hybrid GPS_INPUT+ODOMETRY both active. "ArduPilot ODOMETRY GPS_INPUT companion external visual odometry double-fusion best practice", "fixed-wing UAV high altitude visual odometry 1km AGL accuracy"

Search Query Variants Per Sub-Question

(Selected; full search log preserved in agent transcript and 01_source_registry.md round-2 entries.)

Q-R2-1 (VO):

  1. visual odometry Jetson Orin Nano benchmark 2026 fixed-wing UAV monocular DPVO BASALT OpenVINS SVO Pro
  2. DPVO Deep Patch Visual Odometry Jetson real-time inference benchmark FPS 2025
  3. cuVSLAM Jetson Orin Nano monocular fixed-wing aerial visual odometry CUDA Lucas-Kanade
  4. MASt3R-SLAM monocular real-time 2025 Jetson DROID-SLAM MAC-VO benchmark embedded
  5. VINS-Fusion OpenVINS BASALT SVO Pro Jetson Orin Nano benchmark monocular fixed-wing 2025
  6. DPVO Jetson Orin Nano FPS benchmark monocular visual odometry deployment 2025 ARM
  7. fixed-wing UAV high altitude visual odometry 1km AGL monocular accuracy 2025
  8. Isaac ROS visual SLAM cuVSLAM Jetson Orin Nano monocular fixed-wing UAV high altitude integration
  9. DPV-SLAM DPVO real-time Jetson NX Orin port deployment monocular SLAM 2024

Q-R2-2 (cross-view matcher):

  1. LiteSAM lightweight feature matching satellite aerial imagery 2025 EfficientLoFTR
  2. cross-view UAV satellite image matching benchmark 2025 XoFTR MatchAnything OmniGlue LoFTR LightGlue
  3. MapGlue MapAnything XoFTR cross-modal aerial satellite matching Jetson inference 2025
  4. XFeat lightweight feature matching Jetson Orin TensorRT FPS benchmark 2025
  5. LightGlue ONNX TensorRT Jetson Orin Nano Super fps 2025 SuperPoint inference benchmark
  6. RoMa DKM dense feature matching aerial satellite UAV-VisLoc benchmark accuracy 2025
  7. aerial drone matcher MatchAnything OmniGlue DeDoDe homography benchmark 2025
  8. SuperPoint LightGlue Jetson Orin Nano TensorRT FP16 INT8 ms per frame benchmark
  9. UAV-VisLoc satellite aerial localization SP+LG XFeat LiteSAM RoMa benchmark accuracy meters

Q-R2-3 (EKF / ESKF):

  1. ESKF error state Kalman filter visual inertial navigation drone vs EKF 2025 advantages
  2. ArduPilot EKF3 error state Kalman external visual odometry GPS_INPUT ODOMETRY fusion architecture
  3. ArduPilot EKF3 vs PX4 EKF2 ESKF visual external odometry companion computer architecture
  4. ArduPilot ODOMETRY GPS_INPUT companion external visual odometry double-fusion IMU EKF3 best practice

Q-R2-4 (ortho-tile generator):

  1. orthomosaic generation library python aerial drone OpenDroneMap MicMac OpenSfM real-time
  2. single image orthorectification python library DEM gimbal pinhole homography UAV nadir camera
  3. Orthority orthorectification python single image GeoTIFF DEM RPC frame camera benchmark
  4. Orthority simple-ortho per-frame nadir UAV gimbal pitch roll yaw projection latency milliseconds

Q-R2-5 (sweep):

  1. Cloud Optimized GeoTIFF COG vs MBTiles tile cache embedded UAV onboard storage performance
  2. ROS 2 Humble Jetson Orin Nano Super JetPack 6 MAVROS ArduPilot integration GPS-denied
  3. (Plus targeted re-checks of round-1 components: VPR backbones, MAVLink2 signing, free-threaded Python, SRTM 30 m DEM.)

Completeness Audit

Probe Coverage
Did we re-check every component the user named? ✅ VO (Q-R2-1), matcher (Q-R2-2), EKF (Q-R2-3), ortho (Q-R2-4).
Did we sweep every other component for resource/confidence trade-offs? ✅ VPR (no new entrants — M-33), tile storage (MBTiles WAL stays — M-28), MAVLink (sysid + signing unchanged — M-31), software platform (CPython + ROS-2-vs-DIY surfaced as open Q — M-29, M-32), DEM (no change), camera (already locked).
Did we surface contrarian failure modes per component? ✅ Custom-2-frame-VO is wrong (M-22); LiteSAM-on-Orin-Nano-Super is too slow inline (M-24); RoMa v2 / MASt3R-SLAM are GPU-class (M-25, S62); ArduPilot double-fusion is a bug, not a feature (M-26, M-30).
Did we identify decisions that need user input vs decisions that are deterministic? ✅ ROS 2 vs DIY orchestrator (M-29) — needs user. Channel-split for hybrid (M-30) — recommendation Option A for v1, Option B v1.1+.
Did we re-validate locked AC restrictions (camera, zoom, AC-NEW-7)? ✅ All lock-ins from round 1 carry forward unchanged.
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