LiDAR-Camera Fusion Validation & Debugging
Validate multi-sensor LiDAR-camera fusion and diagnose AV perception failures
What You Can Do
You can systematically validate LiDAR-camera fusion pipelines by analyzing sensor registration, detecting failure modes across object classes and ranges, and diagnosing misalignment issues. This skill provides structured methodologies to verify fusion outputs meet safety-critical accuracy thresholds before validation testing, catching issues like dropped detections, false positives at scene boundaries, and temporal coherence failures that are common sources of AV system failures.
Features
Verify LiDAR-camera extrinsic calibration and detect spatial misalignment artifacts
Identify object class and range-specific detection gaps (pedestrians, cyclists, small vehicles at occlusion boundaries)
Compare early vs. late fusion performance and optimize temporal window sizing
Isolate spurious detections at scene edges and occlusion regions
Map perception performance across Operational Design Domain ranges and conditions
Reconstruct perception system behavior from logged sensor data
Compare performance across different sensor setups and fusion algorithms
Monitor sensor alignment degradation after thermal cycling or hardware reconfiguration
Example Output
Detection Failure Report:
- Pedestrian misses: 8% at 15-25m range, clustered at left edge of camera FOV
- Root cause: LiDAR-camera rotation offset of 1.2° causing point cloud reprojection error
- False positives: 12 per frame in occlusion boundaries (0.3m depth discontinuities)
- Recommendation: Recalibrate extrinsics, increase temporal filtering window to 200ms
Sensor Alignment Visualization:
- LiDAR points overlaid on camera image showing 15-pixel drift in upper regions
- Calibration matrix correction values: Rx=1.2°, Ty=0.08m
- Validation: 94% point cloud reprojection accuracy after correction
ODD Coverage Matrix:
- Range 0-30m: 98.2% pedestrian detection (PASS)
- Range 30-60m: 91.5% detection (marginal for cyclists)
- Night conditions: 73% performance (requires sensor upgrade for ODD claim)
What's Included
- SKILL.md: Complete validation framework with sensor fusion architecture analysis templates
- Fusion Pipeline Diagnostic Checklist: Step-by-step verification protocol for extrinsic calibration, detection performance, and temporal coherence
- Detection Failure Root Cause Analysis Template: Structured worksheet for isolating object class/range-specific gaps
- Sensor Alignment Optimization Workflow: Calibration correction procedure with validation metrics
- ODD Coverage Assessment Matrix: Performance mapping tool for operational design domain validation
Who It's For
- ADAS/Autonomous Vehicle Perception Engineers — validating multi-sensor fusion pipelines for object detection and tracking
- Sensor Fusion Algorithm Developers — optimizing early vs. late fusion architectures and temporal coherence
- Validation & Safety Engineers — conducting pre-deployment perception system verification and ODD coverage assessment
- Hardware Integration Engineers — debugging sensor calibration issues after reconfiguration or thermal cycling
- Incident Investigation Teams — reconstructing perception system failures from logged sensor data
Best For
- Diagnosing detection failures across specific object classes and sensor ranges
- Validating sensor extrinsic calibration and detecting spatial misalignment
- Comparing fusion architecture performance (early vs. late fusion, temporal windowing)
- Preparing perception stacks for Operational Design Domain expansion
- Identifying false positive clusters at scene boundaries and occlusion regions
- Post-incident analysis of AV perception system behavior
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