# Acceptance Test: Chunk Rotation Recovery

## Summary
Validate chunk LiteSAM matching with rotation sweeps for chunks with unknown orientation (sharp turns).

## Linked Acceptance Criteria
**AC-4**: Robust to sharp turns (<5% overlap)
**AC-5**: Connect route chunks

## Preconditions
1. F12 Route Chunk Manager functional
2. F06 Image Rotation Manager with chunk rotation support
3. F09 Metric Refinement with chunk LiteSAM matching
4. F10 Factor Graph Optimizer with chunk merging
5. Test dataset: Chunk with unknown orientation (simulated sharp turn)

## Test Description
Test system's ability to match chunks with unknown orientation using rotation sweeps. When a chunk is created after a sharp turn, its orientation relative to the satellite map is unknown. The system must rotate the entire chunk to all possible angles and attempt LiteSAM matching.

## Test Steps

### Step 1: Create Chunk with Unknown Orientation
- **Action**: Simulate sharp turn scenario
  - Process frames 1-10 (normal flight, heading 0°)
  - Sharp turn at frame 11 (heading changes to 120°)
  - Tracking lost, chunk_2 created proactively
  - Process frames 11-20 in chunk_2
- **Expected Result**:
  - Chunk_2 created with frames 11-20
  - Chunk orientation unknown (previous heading not relevant)
  - Chunk ready for matching (10 frames)

### Step 2: Chunk Semantic Matching
- **Action**: Attempt chunk semantic matching
- **Expected Result**:
  - F08.compute_chunk_descriptor() → aggregate DINOv2 descriptor
  - F08.retrieve_candidate_tiles_for_chunk() → returns top-5 candidate tiles
  - Correct tile in top-5 candidates

### Step 3: Chunk Rotation Sweeps
- **Action**: Attempt chunk LiteSAM matching with rotation sweeps
- **Expected Result**:
  - F06.try_chunk_rotation_steps() called
  - For each rotation angle (0°, 30°, 60°, ..., 330°):
    - F06.rotate_chunk_360() rotates all 10 images
    - F09.align_chunk_to_satellite() attempts matching
  - Match found at 120° rotation (correct orientation)
  - Returns ChunkAlignmentResult with:
    - rotation_angle: 120°
    - chunk_center_gps: correct GPS
    - confidence > 0.7
    - Sim(3) transform computed

### Step 4: Chunk Merging
- **Action**: Merge chunk_2 to main trajectory
- **Expected Result**:
  - F10.add_chunk_anchor() anchors chunk_2
  - F10.merge_chunks() merges chunk_2 into chunk_1
  - Sim(3) transform applied correctly
  - Global trajectory consistent

### Step 5: Verify Final Trajectory
- **Action**: Verify all frames have GPS coordinates
- **Expected Result**:
  - All 20 frames have GPS coordinates
  - Frames 1-10: Original trajectory
  - Frames 11-20: Merged chunk trajectory
  - Global consistency maintained
  - Accuracy: 18/20 < 50m (90%)

## Success Criteria

**Primary Criterion**:
- Chunk rotation sweeps find correct orientation (120°)
- Chunk LiteSAM matching succeeds with rotation
- Chunk merged correctly to main trajectory

**Supporting Criteria**:
- All 12 rotation angles tried
- Match found at correct angle
- Sim(3) transform computed correctly
- Final trajectory globally consistent

## Expected Results

```
Chunk Rotation Recovery:
- Chunk_2 created: frames 11-20
- Unknown orientation: previous heading (0°) not relevant
- Chunk semantic matching: correct tile in top-5
- Rotation sweeps: 12 rotations tried (0°, 30°, ..., 330°)
- Match found: 120° rotation
- Chunk center GPS: Accurate (within 20m)
- Chunk merged: Sim(3) transform applied
- Final trajectory: Globally consistent
- Accuracy: 18/20 < 50m (90%)
```

## Pass/Fail Criteria

**TEST PASSES IF**:
- Chunk rotation sweeps find match at correct angle (120°)
- Chunk LiteSAM matching succeeds
- Chunk merged correctly
- Final trajectory globally consistent
- Accuracy acceptable (≥ 80% < 50m)

**TEST FAILS IF**:
- Rotation sweeps don't find correct angle
- Chunk LiteSAM matching fails
- Chunk merging produces inconsistent trajectory
- Final accuracy below threshold

## Architecture Elements

**Chunk Rotation**:
- F06 rotates all images in chunk by same angle
- 12 rotation steps: 0°, 30°, 60°, ..., 330°
- F09 attempts LiteSAM matching for each rotation

**Chunk LiteSAM Matching**:
- Aggregate correspondences from multiple images
- More robust than single-image matching
- Handles featureless terrain better

**Chunk Merging**:
- Sim(3) transform (translation, rotation, scale)
- Critical for monocular VO scale ambiguity
- Preserves internal consistency

## Notes
- Rotation sweeps are critical for chunks from sharp turns
- Previous heading may not be relevant after sharp turn
- Chunk matching more robust than single-image matching
- Sim(3) transform accounts for scale differences