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docs/03_tests/01_sequential_visual_odometry_integration_spec.md

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+# 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