# Integration Test: Sequential Visual Odometry (Layer 1)

## Summary
Test the SuperPoint + LightGlue sequential tracking pipeline for frame-to-frame relative pose estimation in continuous UAV flight scenarios.

## Component Under Test
**Component**: Sequential Visual Odometry (Layer 1)
**Technologies**: SuperPoint (feature detection), LightGlue (attention-based matching)
**Location**: `gps_denied_07_sequential_visual_odometry`

## Dependencies
- Model Manager (TensorRT models for SuperPoint and LightGlue)
- Image Input Pipeline (preprocessed images)
- Configuration Manager (algorithm parameters)

## Test Scenarios

### Scenario 1: Normal Sequential Tracking
**Input Data**:
- Images: AD000001.jpg through AD000010.jpg (10 consecutive images)
- Ground truth: coordinates.csv
- Camera parameters: data_parameters.md (400m altitude, 25mm focal length)

**Expected Output**:
- Relative pose transformations between consecutive frames
- Feature match count >100 matches per frame pair
- Inlier ratio >70% after geometric verification
- Translation vectors consistent with ~120m spacing

**Maximum Execution Time**: 100ms per frame pair

**Success Criteria**:
- All 9 frame pairs successfully matched
- Estimated relative translations within 20% of ground truth distances
- Rotation estimates within 5 degrees of expected values

### Scenario 2: Low Overlap (<5%)
**Input Data**:
- Images: AD000042, AD000044, AD000045 (sharp turn with gap)
- Sharp turn causes minimal overlap between AD000042 and AD000044

**Expected Output**:
- LightGlue adaptive depth mechanism activates (more layers)
- Lower match count (10-50 matches) but high confidence
- System reports low confidence flag for downstream fusion

**Maximum Execution Time**: 200ms per difficult frame pair

**Success Criteria**:
- At least 10 high-quality matches found
- Inlier ratio >50% despite low overlap
- Confidence metric accurately reflects matching difficulty

### Scenario 3: Repetitive Agricultural Texture
**Input Data**:
- Images from AD000015-AD000025 (likely agricultural fields)
- High texture repetition challenge

**Expected Output**:
- SuperPoint detects semantically meaningful features (field boundaries, roads)
- LightGlue dustbin mechanism rejects ambiguous matches
- Stable tracking despite texture repetition

**Maximum Execution Time**: 100ms per frame pair

**Success Criteria**:
- Match count >80 per frame pair
- No catastrophic matching failures (>50% outliers)
- Tracking continuity maintained across sequence

## Performance Requirements
- SuperPoint inference: <20ms per image (RTX 2060/3070)
- LightGlue matching: <80ms per frame pair
- Combined pipeline: <100ms per frame (normal overlap)
- TensorRT FP16 optimization mandatory

## Quality Metrics
- Match count: Mean >100, Min >50 (normal overlap)
- Inlier ratio: Mean >70%, Min >50%
- Feature distribution: >30% of image area covered
- Geometric consistency: Epipolar error <1.0 pixels