docs: add requirements and roadmap

36 v1 requirements across 6 categories (ESKF, VO, SAT, GPR, MAV, PIPE, TEST).
7-phase roadmap ordered by dependency: ESKF → VO → Satellite → MAVLink → Pipeline → SITL → Validation.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.planning/ROADMAP.md

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+# Roadmap: GPS-Denied Onboard Navigation System
+
+## Overview
+
+The scaffold exists (~2800 lines): FastAPI service, all component ABCs, Pydantic schemas, database layer, and SSE streaming are in place. What is missing is every algorithmic kernel. This roadmap implements them in dependency order: the ESKF math core first (everything else feeds into it), then the two sensor inputs (VO and satellite/GPR), then the MAVLink output that closes the loop to the flight controller, then end-to-end pipeline wiring, then a Docker SITL test harness, and finally accuracy validation against real flight data.
+
+## Phases
+
+- [ ] **Phase 1: ESKF Core** - 15-state error-state Kalman filter, coordinate transforms, confidence scoring
+- [ ] **Phase 2: Visual Odometry** - cuVSLAM wrapper (Jetson) + OpenCV ORB stub (dev/CI) + TRT SuperPoint/LightGlue
+- [ ] **Phase 3: Satellite Matching + GPR** - XFeat TRT matching, offline tile pipeline, real Faiss GPR index
+- [ ] **Phase 4: MAVLink I/O** - pymavlink GPS_INPUT output loop, IMU input listener, telemetry, re-localization request
+- [ ] **Phase 5: End-to-End Pipeline Wiring** - processor integration, GTSAM factor graph, recovery coordinator, object localization
+- [ ] **Phase 6: Docker SITL Harness + CI** - ArduPilot SITL, camera replay, tile server mock, CI integration
+- [ ] **Phase 7: Accuracy Validation** - 60-frame dataset validation, latency profiling, blackbox test suite
+
+## Phase Details
+
+### Phase 1: ESKF Core
+**Goal**: A correct, standalone ESKF implementation exists that fuses IMU, VO, and satellite measurements and outputs confidence-tiered position estimates in WGS84
+**Depends on**: Nothing (first phase — no other algorithmic component depends on this being absent)
+**Requirements**: ESKF-01, ESKF-02, ESKF-03, ESKF-04, ESKF-05, ESKF-06
+**Success Criteria** (what must be TRUE):
+  1. ESKF propagates nominal state (position, velocity, quaternion, biases) from synthetic IMU inputs and covariance grows correctly between measurement updates
+  2. VO measurement update reduces position uncertainty and innovation is within expected bounds for a simulated relative pose input
+  3. Satellite measurement update corrects absolute position and covariance tightens to satellite noise level
+  4. Confidence tier outputs HIGH when last satellite correction is recent and covariance is small, MEDIUM on VO-only, LOW on IMU-only — verified by unit tests
+  5. Full coordinate chain (pixel → camera ray → body → NED → WGS84) produces correct GPS coordinates for a known geometry test case; all FAKE Math stubs replaced
+**Plans**: TBD
+
+### Phase 2: Visual Odometry
+**Goal**: VO produces metric relative poses via cuVSLAM on Jetson and via OpenCV ORB on dev/CI, both satisfying the same interface — no more scale-ambiguous unit vectors
+**Depends on**: Phase 1 (ESKF provides metric scale reference and coordinate transforms for VO measurement update)
+**Requirements**: VO-01, VO-02, VO-03, VO-04, VO-05
+**Success Criteria** (what must be TRUE):
+  1. cuVSLAM wrapper initializes in Inertial mode with camera intrinsics and IMU parameters, and returns RelativePose with `scale_ambiguous=False` and metric translation in NED
+  2. OpenCV ORB stub satisfies the same ISequentialVisualOdometry interface and passes the same interface contract tests as the cuVSLAM wrapper
+  3. TRT SuperPoint/LightGlue engines load and run inference on Jetson; MockInferenceEngine is selected automatically on dev/x86
+  4. ImageInputPipeline accepts single-image batches without error; sequence lookup returns the correct frame with no substring collision
+**Plans**: TBD
+
+### Phase 3: Satellite Matching + GPR
+**Goal**: The system can correct absolute position from pre-loaded satellite tiles and re-localize after tracking loss using a real Faiss descriptor index
+**Depends on**: Phase 1 (ESKF position uncertainty drives tile selection radius and measurement update), Phase 2 (VO provides keyframe selection timing)
+**Requirements**: SAT-01, SAT-02, SAT-03, SAT-04, SAT-05, GPR-01, GPR-02, GPR-03
+**Success Criteria** (what must be TRUE):
+  1. Satellite tile selection queries the local GeoHash-indexed directory using ESKF position ± 3σ and returns correct tiles without any HTTP requests
+  2. Camera frame is GSD-normalized to satellite resolution before matching; XFeat TRT inference runs on Jetson and MockInferenceEngine on dev/CI
+  3. RANSAC homography produces a WGS84 position estimate with a confidence score derived from inlier ratio, accepted by ESKF satellite measurement update
+  4. GPR loads a real Faiss index from disk and returns tile candidates ranked by DINOv2 descriptor similarity (not random vectors)
+  5. After simulated tracking loss, GPR candidate + MetricRefinement produces an ESKF re-localization within expected accuracy bounds
+**Plans**: TBD
+
+### Phase 4: MAVLink I/O
+**Goal**: The flight controller receives GPS_INPUT at 5-10Hz and the system receives IMU data from the flight controller — the primary acceptance criterion is met end-to-end for the communication layer
+**Depends on**: Phase 1 (ESKF state is the source for GPS_INPUT field population; IMU data drives ESKF prediction)
+**Requirements**: MAV-01, MAV-02, MAV-03, MAV-04, MAV-05
+**Success Criteria** (what must be TRUE):
+  1. pymavlink sends GPS_INPUT messages to a MAVLink endpoint at 5-10Hz; all required fields populated (lat, lon, alt, velocity, accuracy, fix_type, hdop, vdop, GPS time)
+  2. fix_type maps correctly from ESKF confidence tier: HIGH → 3 (3D fix), MEDIUM → 2 (2D fix), LOW → 0 (no fix)
+  3. IMU listener receives ATTITUDE/RAW_IMU from flight controller at 5-10Hz and ESKF prediction step runs at that rate between camera frames
+  4. After 3 consecutive frames with no position estimate, a MAVLink NAMED_VALUE_FLOAT message with last known position is sent (verifiable in SITL logs)
+  5. Telemetry at 1Hz emits confidence score and drift estimate to ground station via NAMED_VALUE_FLOAT
+**Plans**: TBD
+
+### Phase 5: End-to-End Pipeline Wiring
+**Goal**: A single uploaded camera frame travels through the full pipeline — VO, ESKF update, satellite correction (on keyframes), GPS_INPUT output — with no hardcoded stubs in the path
+**Depends on**: Phase 1, Phase 2, Phase 3, Phase 4 (all algorithmic components must exist to be wired)
+**Requirements**: PIPE-01, PIPE-02, PIPE-03, PIPE-04, PIPE-05, PIPE-06, PIPE-07, PIPE-08
+**Success Criteria** (what must be TRUE):
+  1. process_frame executes the full chain without error: VO relative pose → ESKF VO update → (every 5-10 frames) satellite match → ESKF satellite update → GPS_INPUT sent to flight controller
+  2. SatelliteDataManager and CoordinateTransformer are instantiated in app.py lifespan and injected into the processor; no component is standalone
+  3. FactorGraphOptimizer calls real GTSAM ISAM2 update when GTSAM is available; mock path remains for CI
+  4. Object GPS localization (POST /objects/locate) returns a WGS84 position using the real pixel→ray→ground chain; hardcoded (48.0, 37.0) stub is gone
+  5. Application starts without TypeError; ImageRotationManager constructor accepts the model manager argument
+**Plans**: TBD
+
+### Phase 6: Docker SITL Harness + CI
+**Goal**: The full pipeline can be tested in a reproducible Docker environment with ArduPilot SITL, camera replay, and a tile server mock — and CI runs this on every commit
+**Depends on**: Phase 5 (all components must be wired before integration testing is meaningful)
+**Requirements**: TEST-01, TEST-02
+**Success Criteria** (what must be TRUE):
+  1. `docker compose up` starts ArduPilot SITL, the GPS-denied service, a camera-replay container, and a satellite tile server mock — all communicate over MAVLink and HTTP
+  2. CI pipeline runs on x86 using OpenCV ORB stub and MockInferenceEngine; all 85+ unit tests pass with no manual steps
+  3. MAVLink GPS_INPUT messages are captured in SITL logs and show 5-10Hz output rate during camera replay
+  4. Tracking loss scenario (simulated by replaying frames with no overlap) triggers RECOVERY state and sends re-localization request
+**Plans**: TBD
+
+### Phase 7: Accuracy Validation
+**Goal**: The system demonstrably meets the navigation accuracy acceptance criteria on the 60-frame test dataset, and all 21 blackbox test scenarios are implemented as runnable tests
+**Depends on**: Phase 6 (SITL harness is required for the blackbox test scenarios)
+**Requirements**: TEST-03, TEST-04, TEST-05
+**Success Criteria** (what must be TRUE):
+  1. Running against AD000001–AD000060.jpg with coordinates.csv ground truth: 80% of frames within 50m error and 60% of frames within 20m error
+  2. Maximum cumulative VO drift between satellite corrections is less than 100m across any segment in the test dataset
+  3. End-to-end latency per frame (camera capture to GPS_INPUT) is under 400ms on Jetson, with a breakdown report per pipeline stage
+  4. All 21 blackbox test scenarios (FT-P-01 to FT-P-14, FT-N-01 to FT-N-07) run as pytest tests against the SITL harness and produce a pass/fail report
+**Plans**: TBD
+
+## Progress
+
+| Phase | Plans Complete | Status | Completed |
+|-------|----------------|--------|-----------|
+| 1. ESKF Core | 0/TBD | Not started | - |
+| 2. Visual Odometry | 0/TBD | Not started | - |
+| 3. Satellite Matching + GPR | 0/TBD | Not started | - |
+| 4. MAVLink I/O | 0/TBD | Not started | - |
+| 5. End-to-End Pipeline Wiring | 0/TBD | Not started | - |
+| 6. Docker SITL Harness + CI | 0/TBD | Not started | - |
+| 7. Accuracy Validation | 0/TBD | Not started | - |