feat(phases 2-7): implement full GPS-denied navigation pipeline

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>

src/gps_denied/core/pipeline.py

@@ -28,9 +28,11 @@ class ImageInputPipeline:
         # flight_id -> asyncio.Queue of ImageBatch
         self._queues: dict[str, asyncio.Queue] = {}
         self.max_queue_size = max_queue_size
-        
+
         # In-memory tracking (in a real system, sync this with DB)
         self._status: dict[str, dict] = {}
+        # Exact sequence → filename mapping (VO-05: no substring collision)
+        self._sequence_map: dict[str, dict[int, str]] = {}
 
     def _get_queue(self, flight_id: str) -> asyncio.Queue:
         if flight_id not in self._queues:
@@ -50,7 +52,7 @@ class ImageInputPipeline:
         errors = []
         
         num_images = len(batch.images)
-        if num_images < 10:
+        if num_images < 1:
             errors.append("Batch is empty")
         elif num_images > 100:
             errors.append("Batch too large")
@@ -124,6 +126,8 @@ class ImageInputPipeline:
                 metadata=meta
             )
             processed_images.append(img_data)
+            # VO-05: record exact sequence→filename mapping
+            self._sequence_map.setdefault(flight_id, {})[seq] = fn
             
         # Store to disk
         self.store_images(flight_id, processed_images)
@@ -161,19 +165,33 @@ class ImageInputPipeline:
         return img
 
     def get_image_by_sequence(self, flight_id: str, sequence: int) -> ImageData | None:
-        """Retrieves a specific image by sequence number."""
-        # For simplicity, we assume filenames follow "frame_{sequence:06d}.jpg" 
-        # But if the user uploaded custom files, we'd need a DB lookup.
-        # Let's use a local map for this prototype if it's strictly required, 
-        # or search the directory.
+        """Retrieves a specific image by sequence number (exact match — VO-05)."""
         flight_dir = os.path.join(self.storage_dir, flight_id)
         if not os.path.exists(flight_dir):
             return None
-            
-        # search
+
+        # Prefer the exact mapping built during process_next_batch
+        fn = self._sequence_map.get(flight_id, {}).get(sequence)
+        if fn:
+            path = os.path.join(flight_dir, fn)
+            img = cv2.imread(path)
+            if img is not None:
+                h, w = img.shape[:2]
+                meta = ImageMetadata(
+                    sequence=sequence,
+                    filename=fn,
+                    dimensions=(w, h),
+                    file_size=os.path.getsize(path),
+                    timestamp=datetime.now(timezone.utc),
+                )
+                return ImageData(flight_id, sequence, fn, img, meta)
+
+        # Fallback: scan directory for exact filename patterns
+        # (handles images stored before this process started)
         for fn in os.listdir(flight_dir):
-            # very rough matching
-            if str(sequence) in fn or fn.endswith(f"_{sequence:06d}.jpg"):
+            base, _ = os.path.splitext(fn)
+            # Accept only if the base name ends with exactly the padded sequence number
+            if base.endswith(f"{sequence:06d}") or base == str(sequence):
                 path = os.path.join(flight_dir, fn)
                 img = cv2.imread(path)
                 if img is not None:
@@ -183,10 +201,10 @@ class ImageInputPipeline:
                         filename=fn,
                         dimensions=(w, h),
                         file_size=os.path.getsize(path),
-                        timestamp=datetime.now(timezone.utc)
+                        timestamp=datetime.now(timezone.utc),
                     )
                     return ImageData(flight_id, sequence, fn, img, meta)
-                    
+
         return None
 
     def get_processing_status(self, flight_id: str) -> ProcessingStatus: