gps-denied-onboard/_docs/00_research/06_component_fit_matrix.md

Component Fit Matrix

Candidate	Intended Role	Project Constraints Checked	Evidence	Mismatches / Disqualifiers	Status	Decision Rationale
Bounded latest-frame scheduler	Runtime flow control and deadline ownership	3 Hz input, <400 ms p95 latency, <=10% frame drop allowance, no batching	Fact #27	Must account for dropped frames and preserve timestamp correctness	Selected	Prevents stale FIFO backlog and makes AC-4.1/AC-4.4 implementable
Custom planar VO/IMU module	Relative motion between satellite anchors	Fixed nadir monocular camera, flat-terrain assumption, FC attitude/altitude, 400 ms p95, no raw frame storage	Facts #4, #9, #20	Requires calibration, time sync, covariance model, and Jetson benchmark	Selected	Best exact fit for v1 sensor geometry without stereo/GPL dependency
NVIDIA cuVSLAM	Relative VO/VIO	Jetson support, IMU fallback, visual odometry	Facts #7, #20	Official docs describe stereo-visual-inertial assumptions; IMU-only degraded tracking is short-duration	Rejected	Good Jetson stack but wrong v1 camera/input assumptions
ORB-SLAM3	Mono-inertial SLAM baseline	Monocular+IMU support	Fact #8	GPLv3 product risk; generic SLAM assumptions not tailored to nadir fixed-wing	Experimental only	Useful offline benchmark, not selected product dependency
VINS-Fusion	Mono-inertial estimator baseline	Monocular+IMU support	Fact #8	GPL-3.0 product risk; ROS/research integration burden	Experimental only	Useful reference, not selected product dependency
AnyLoc/DINOv2-VLAD style descriptors	Coarse VPR over precomputed chunks	Offline descriptor generation, event-triggered online query, sparse terrain, cache cap	Facts #5, #6, #10, #15, #19	Raw 49,152-dimensional descriptors can violate memory/cache budgets unless compressed	Selected with compression gate	Strong fit as conditional top-K retrieval layer only after descriptor/index-size proof
FAISS CPU/HNSW/Flat	VPR vector retrieval	Jetson ARM64, local cache, bounded chunk index	Fact #15	GPU packages not assumed; source-built GPU optional only	Selected	Conservative v1 baseline
FAISS GPU/cuVS	Accelerated VPR retrieval	CUDA Jetson, lower latency	Fact #15	ARM64 GPU support requires source build and validation	Experimental only	Optimization path, not v1 assumption
Official Magic Leap SuperPoint pretrained weights	Local feature extraction	Cross-view candidate refinement	Fact #17	Noncommercial research-only license blocks product use without separate commercial permission	Rejected	Do not select as v1 product dependency by default
ALIKED + LightGlue + RANSAC	Learned local satellite/UAV geometric match	Cross-view candidate refinement, inlier/covariance gate, license separation	Facts #18, #24	Jetson speed and sparse-steppe accuracy still need benchmark	Selected candidate	Concrete license-cleared learned-feature path for v1 planning
SIFT/AKAZE + classical matching fallback	License-safe local matching fallback	No special model weights, CPU/GPU fallback	Fact #10	May be weaker on cross-domain imagery and sparse fields	Selected as fallback	Keeps implementation unblocked and provides a legal baseline
DeDoDe	Learned local feature fallback	MIT-licensed model family and deployment ports	Fact #25	Model size/runtime and DINOv2-related variants need validation	Experimental only	Useful fallback if ALIKED/SIFT miss accuracy or robustness targets
ESKF in local NED/ENU	State fusion and covariance owner	IMU propagation, VO, satellite anchors, false-position budget, GPS_INPUT accuracy	Facts #1, #2, #9, #10	Requires calibration and Monte Carlo validation	Selected	Necessary to satisfy confidence and safety ACs
pymavlink GPS_INPUT emitter	Primary FC output	ArduPilot-only v1, GPS substitute framing, WGS84 output	Facts #1, #2, #3	Must configure EKF/GPS params and validate in SITL	Selected	Safest v1 output path
MAVLink ODOMETRY auxiliary	Rich covariance/yaw external nav	ArduPilot EKF3 external nav	Fact #3	Source-fusion/version risk; can double-fuse if misconfigured	Needs user decision for v1.1	Deferred until SITL confirms exact release behavior
FastAPI local API	Health/session/object localization API	Python CV stack, OpenAPI docs, local-only service	Fact #14	Must stay out of hot frame path	Selected	Satisfies API documentation and integration needs
SQLite/MBTiles-like package	Onboard tile lookup and metadata	Offline cache, random lookup, 10 GB cap, freshness metadata	Facts #13, #16, #21	Must handle corruption, sidecar schema, and measured descriptor/index budget	Selected with storage gate	Good embedded local cache shape, but the 10 GB budget needs a packing benchmark
COG/GeoTIFF exchange	Satellite Service import/export	Geospatial processing, provider imagery, tile write-back ingestion	Facts #16, #21	Not selected as sole hot lookup format; compression ratio must be measured	Selected	Good boundary format with Satellite Service
AerialVL / UAV-VisLoc adapters	Early VPR and cross-view validation data	Public aerial localization data with satellite references	Facts #22, #23	Partial match only; not enough for FC IMU/MAVLink acceptance	Selected for early validation	Useful benchmark inputs, not final acceptance evidence