mirror of
https://github.com/azaion/gps-denied-onboard.git
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Yuzviak
094895b21b
Phase 2 — Visual Odometry: - ORBVisualOdometry (dev/CI), CuVSLAMVisualOdometry (Jetson) - TRTInferenceEngine (TensorRT FP16, conditional import) - create_vo_backend() factory Phase 3 — Satellite Matching + GPR: - SatelliteDataManager: local z/x/y tiles, ESKF ±3σ tile selection - GSD normalization (SAT-03), RANSAC inlier-ratio confidence (SAT-04) - GlobalPlaceRecognition: Faiss index + numpy fallback Phase 4 — MAVLink I/O: - MAVLinkBridge: GPS_INPUT 15+ fields, IMU callback, 1Hz telemetry - 3-consecutive-failure reloc request - MockMAVConnection for CI Phase 5 — Pipeline Wiring: - ESKF wired into process_frame: VO update → satellite update - CoordinateTransformer + SatelliteDataManager via DI - MAVLink state push per frame (PIPE-07) - Real pixel_to_gps via ray-ground projection (PIPE-06) - GTSAM ISAM2 update when available (PIPE-03) Phase 6 — Docker + CI: - Multi-stage Dockerfile (python:3.11-slim) - docker-compose.yml (dev), docker-compose.sitl.yml (ArduPilot SITL) - GitHub Actions: ci.yml (lint+pytest+docker smoke), sitl.yml (nightly) - tests/test_sitl_integration.py (8 tests, skip without SITL) Phase 7 — Accuracy Validation: - AccuracyBenchmark + SyntheticTrajectory - AC-PERF-1: 80% within 50m ✅ - AC-PERF-2: 60% within 20m ✅ - AC-PERF-3: p95 latency < 400ms ✅ - AC-PERF-4: VO drift 1km < 100m ✅ (actual ~11m) - scripts/benchmark_accuracy.py CLI Tests: 195 passed / 8 skipped Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
"""Metric Refinement schemas (Component F09)."""
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from typing import Optional
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import numpy as np
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from pydantic import BaseModel
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from gps_denied.schemas import GPSPoint
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class AlignmentResult(BaseModel):
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"""Result of aligning a UAV image to a single satellite tile."""
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model_config = {"arbitrary_types_allowed": True}
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matched: bool
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homography: np.ndarray # (3, 3)
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gps_center: GPSPoint
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confidence: float
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inlier_count: int
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total_correspondences: int
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reprojection_error: float # Mean error in pixels
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class Sim3Transform(BaseModel):
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"""Sim(3) transformation: scale, rotation, translation."""
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model_config = {"arbitrary_types_allowed": True}
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translation: np.ndarray # (3,)
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rotation: np.ndarray # (3, 3) rotation matrix
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scale: float
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class ChunkAlignmentResult(BaseModel):
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"""Result of aligning a chunk array of UAV images to a satellite tile."""
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model_config = {"arbitrary_types_allowed": True}
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matched: bool
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chunk_id: str
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chunk_center_gps: GPSPoint
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rotation_angle: float
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confidence: float
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inlier_count: int
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transform: Sim3Transform
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reprojection_error: float
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class LiteSAMConfig(BaseModel):
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"""Configuration for LiteSAM alignment."""
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model_path: str = "mock_path"
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confidence_threshold: float = 0.7
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min_inliers: int = 15
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max_reprojection_error: float = 2.0 # pixels
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multi_scale_levels: int = 3
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chunk_min_inliers: int = 30
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