gps-denied-onboard/_docs/00_research/02_fact_cards.md

Fact Cards

Fact #1

• Statement: ArduPilot MAVProxy GPSInput requires GPS1_TYPE=14 to accept MAVLink GPS input.
• Source: Source #1
• Phase: Phase 2
• Target Audience: ArduPilot v1 integration
• Confidence: High
• Related Dimension: Flight-controller output
• Fit Impact: Supports GPS_INPUT v1 selection

Fact #2

• Statement: MAVLink GPS_INPUT is a raw GPS sensor input message, not the global position estimate of the system.
• Source: Source #2
• Phase: Phase 2
• Target Audience: MAVLink integrators
• Confidence: High
• Related Dimension: Output semantics and covariance
• Fit Impact: Requires careful accuracy/covariance fields and FC EKF configuration

Fact #3

• Statement: ArduPilot issue #30076 documented EKF3 instability when external navigation and GPS were fused unexpectedly; the issue is version-specific and marked closed, but it proves the risk class is real.
• Source: Source #3
• Phase: Phase 2
• Target Audience: ArduPilot v1/v1.1 release planning
• Confidence: Medium
• Related Dimension: Autopilot source fusion
• Fit Impact: Supports GPS_INPUT-only v1 and SITL gate before ODOMETRY

Fact #4

• Statement: Jetson Orin Nano Super officially provides 67 sparse TOPS, 8 GB LPDDR5, 102 GB/s memory bandwidth, and a 25 W mode.
• Source: Source #4
• Phase: Phase 2
• Target Audience: Onboard runtime sizing
• Confidence: High
• Related Dimension: Hardware envelope
• Fit Impact: Supports feasibility, but memory/thermal profiling remains mandatory

Fact #5

• Statement: NVIDIA reports TensorRT FP16 ViT benchmark rates on Orin Nano 8 GB Super Mode, including DINOv2-base-patch14 around 126 FPS in the published table.
• Source: Source #5
• Phase: Phase 2
• Target Audience: VPR runtime planning
• Confidence: Medium
• Related Dimension: VPR model feasibility
• Fit Impact: Supports conditional DINOv2 VPR, not per-frame full pipeline guarantee

Fact #6

• Statement: A TensorRT issue report measured DINOv2-S on Jetson Orin at roughly 22-23 ms GPU compute with limited INT8 speedup.
• Source: Source #19
• Phase: Phase 2
• Target Audience: Jetson VPR deployment
• Confidence: Medium
• Related Dimension: Model optimization risk
• Fit Impact: Requires project benchmark; INT8 speedup must not be assumed

Fact #7

• Statement: NVIDIA cuVSLAM is a GPU-accelerated stereo-visual-inertial SLAM and odometry library; it can use IMU fallback but is designed around stereo camera input.
• Source: Source #6
• Phase: Phase 2
• Target Audience: VO/VIO component selection
• Confidence: High
• Related Dimension: Required camera inputs
• Fit Impact: Reject as lead v1 VO for a single fixed monocular nav camera

Fact #8

• Statement: ORB-SLAM3 and VINS-Fusion support monocular-inertial modes, but both are GPL-family licensed.
• Source: Source #7, Source #8
• Phase: Phase 2
• Target Audience: Product dependency selection
• Confidence: High
• Related Dimension: Licensing and integration
• Fit Impact: Experimental/reference only unless legal approval is obtained

Fact #9

• Statement: A 2024 fixed-wing UAV study used satellite imagery to reduce visual odometry accumulated error over missions above 1000 m and over 17 km.
• Source: Source #12
• Phase: Phase 2
• Target Audience: Architecture feasibility
• Confidence: High
• Related Dimension: Satellite-aided VO
• Fit Impact: Supports hybrid VO + satellite anchor architecture

Fact #10

• Statement: Recent UAV-to-satellite cross-view localization research targets source-domain appearance differences and similar-scene interference, confirming these are core risks.
• Source: Source #13
• Phase: Phase 2
• Target Audience: Cross-view matching design
• Confidence: High
• Related Dimension: False-match risk
• Fit Impact: Requires top-K VPR, local geometric verification, covariance gating, and stale-tile controls

Fact #11

• Statement: Airbus Pléiades Neo advertises native 30 cm imagery and 3.5 m CE90 location accuracy.
• Source: Source #14
• Phase: Phase 2
• Target Audience: Satellite Service SLA
• Confidence: High
• Related Dimension: Reference imagery resolution
• Fit Impact: Supports 0.3 m/px ideal cache target

Fact #12

• Statement: Vantor/Maxar states its constellation provides 30 cm-class imagery and native <5 m CE90 accuracy.
• Source: Source #15
• Phase: Phase 2
• Target Audience: Satellite Service SLA
• Confidence: High
• Related Dimension: Reference imagery resolution
• Fit Impact: Supports 0.3 m/px ideal cache target

Fact #13

• Statement: OpenStreetMap zoom-level math gives meters-per-pixel at the equator and requires multiplying by cosine(latitude); zoom alone does not define physical pixel size.
• Source: Source #16
• Phase: Phase 2
• Target Audience: Cache engineering
• Confidence: High
• Related Dimension: Tile resolution convention
• Fit Impact: Supports explicit pixel-size cache contract

Fact #14

• Statement: FastAPI automatically exposes interactive API docs and an OpenAPI schema.
• Source: Source #17
• Phase: Phase 2
• Target Audience: Local API design
• Confidence: High
• Related Dimension: OpenAPI documentation
• Fit Impact: Supports Python/FastAPI for local health/session/object API

Fact #15

• Statement: FAISS GPU packages should not be assumed on Jetson ARM64; CPU FAISS or source-built GPU FAISS must be validated.
• Source: Source #11
• Phase: Phase 2
• Target Audience: VPR index deployment
• Confidence: Medium
• Related Dimension: Index runtime
• Fit Impact: Select CPU FAISS/HNSW-flat as v1 baseline; GPU FAISS is optimization only

Fact #16

• Statement: COG is a standard GeoTIFF profile useful for geospatial processing, while MBTiles-style SQLite tile packages are better aligned with local offline tile lookup.
• Source: Source #18 plus offline cache search
• Phase: Phase 2
• Target Audience: Cache storage
• Confidence: Medium
• Related Dimension: Cache format
• Fit Impact: Select COG/GeoTIFF for Satellite Service exchange and SQLite/MBTiles-like package for onboard lookup/index sidecars

Fact #17

• Statement: Official Magic Leap SuperPoint pretrained weights are restricted to academic or non-profit noncommercial research use.
• Source: Source #20
• Phase: Mode B Assessment
• Target Audience: Product dependency selection
• Confidence: High
• Related Dimension: Local matcher licensing
• Fit Impact: Reject official SuperPoint weights as a v1 product dependency unless a commercial license is obtained

Fact #18

• Statement: LightGlue's Apache-2.0 license does not automatically license upstream feature extractors; extractor weights must be reviewed separately.
• Source: Source #21
• Phase: Mode B Assessment
• Target Audience: Product dependency selection
• Confidence: High
• Related Dimension: Local matcher licensing
• Fit Impact: Select LightGlue only behind a license-cleared extractor interface

Fact #19

• Statement: AnyLoc DINOv2 VLAD examples produce 49,152-dimensional descriptors, so a large multi-scale VPR gallery can become a memory/storage problem if descriptors are stored uncompressed.
• Source: Source #22
• Phase: Mode B Assessment
• Target Audience: VPR/cache developers
• Confidence: High
• Related Dimension: VPR descriptor footprint
• Fit Impact: Requires PCA/quantization or smaller descriptors before the approach can satisfy the 8 GB memory and 10 GB cache budgets

Fact #20

• Statement: NVIDIA describes cuVSLAM as stereo-visual-inertial SLAM/odometry and documents IMU-only degraded tracking as suitable only for short intervals around one second.
• Source: Source #23
• Phase: Mode B Assessment
• Target Audience: VO/VIO component selection
• Confidence: High
• Related Dimension: Required camera inputs and fallback duration
• Fit Impact: Keep cuVSLAM rejected as a v1 lead dependency for the fixed monocular navigation camera, but keep it as a Jetson benchmark/reference if hardware changes

Fact #21

• Statement: COG supports tiled storage, overviews, and multiple compression profiles, but the docs do not define a universal bytes-per-pixel budget for 0.3 m satellite imagery.
• Source: Source #24
• Phase: Mode B Assessment
• Target Audience: Cache engineers
• Confidence: High
• Related Dimension: Satellite cache storage
• Fit Impact: Treat the 10 GB cache as a measured acceptance gate, not as proven by zoom-level math

Fact #22

• Statement: AerialVL provides public aerial localization trajectories with RGB imagery, GNSS, and satellite reference patches, but it is only a partial match for the fixed-wing/ArduPilot/IMU deployment target.
• Source: Source #25
• Phase: Mode B Assessment
• Target Audience: Validation planning
• Confidence: Medium
• Related Dimension: Dataset realism
• Fit Impact: Use public datasets for early VPR and cross-view benchmarks, but require SITL or real FC IMU traces for final fusion validation

Fact #23

• Statement: UAV-VisLoc provides UAV images, satellite maps, and metadata such as coordinates, altitude, heading, and capture date, but does not replace the need for project-specific IMU/camera timing traces.
• Source: Source #26
• Phase: Mode B Assessment
• Target Audience: Validation planning
• Confidence: Medium
• Related Dimension: Dataset realism
• Fit Impact: Add dataset adapters for retrieval/localization tests while keeping final acceptance tied to project replay and ArduPilot SITL

Fact #24

• Statement: LightGlue supports ALIKED, DISK, SIFT, and other extractors, so the local matcher can name concrete license-cleared candidates instead of an abstract "license-cleared extractor."
• Source: Source #27
• Phase: Mode B Round 2
• Target Audience: Local matcher productization
• Confidence: High
• Related Dimension: Local feature selection
• Fit Impact: Select ALIKED + LightGlue and OpenCV SIFT/AKAZE as concrete v1 candidates; keep SuperPoint rejected unless licensed

Fact #25

• Statement: DeDoDe is MIT-licensed and has ONNX/TensorRT deployment ports, making it a plausible learned-feature fallback, but its model size and DINOv2-related variants still require Jetson validation.
• Source: Source #28
• Phase: Mode B Round 2
• Target Audience: Local matcher productization
• Confidence: Medium
• Related Dimension: Local feature selection
• Fit Impact: Mark DeDoDe as experimental fallback until runtime and cross-domain accuracy are measured

Fact #26

• Statement: SIFT is available in OpenCV's main features2d module after patent expiration, supporting its use as a commercial-safe classical baseline.
• Source: Source #29
• Phase: Mode B Round 2
• Target Audience: Local matcher productization
• Confidence: High
• Related Dimension: Classical matching baseline
• Fit Impact: Select OpenCV SIFT/AKAZE as the legal baseline for local geometric verification and regression tests

Fact #27

• Statement: With 3 Hz camera input and <400 ms p95 output latency, a FIFO frame queue can violate latency even when every component is individually fast enough.
• Source: Derived from AC-4.1 and AC-4.4 timing constraints
• Phase: Mode B Round 2
• Target Audience: Real-time pipeline design
• Confidence: High
• Related Dimension: Runtime scheduling
• Fit Impact: Add a bounded latest-frame scheduler and explicit drop/backpressure policy to the architecture