Own the core measurement engine that converts LiDAR + camera input into sub-millimetre CAD-aligned dimensional intelligence. You are responsible for the accuracy layer that the entire D2R platform stands on – if alignment drifts by 3mm, the product's value collapses.
System Ownership
- Primary: 3D perception pipeline (point cloud acquisition → registration → alignment → deviation output)
- Primary: CAD-to-scan alignment engine (ICP variants, global registration, RMS scoring)
- Primary: Sensor fusion layer (LiDAR + RGB-D + IMU synchronisation and calibration)
- Secondary interface: Edge AI team (your alignment outputs feed their inference models)
- Secondary interface: MR team (your deviation maps drive their overlay rendering)
- Does NOT own: On-device model deployment (Edge AI), MR rendering pipeline, cloud analytics
What You Will Build
- 3D registration engine – ICP-based point-to-point and point-to-plane alignment between CAD geometry and real-world LiDAR scans. Handle symmetry ambiguity in repeated structural elements (beams, pipes, flanges).
- Point cloud processing pipeline – Multi-resolution processing for scan data exceeding 1M points per cycle. Voxel downsampling, statistical outlier removal, normal estimation at scale.
- Depth calibration & sensor fusion – Temporal and spatial synchronisation of LiDAR, RGB-D, and IMU streams. Compensate for sensor desync that causes ghost points.
- Deviation detection & tolerance mapping – Compute per-point and per-feature deviations against CAD reference. Generate deviation heatmaps with GD&T-aware; tolerance bands.
- Large-scale object accuracy – Maintain sub-3mm alignment accuracy on structures exceeding 2 metres. Solve drift compensation for large-scale industrial objects.
- On-device inference optimisation – Collaborate with Edge AI to ensure perception models meet the 200ms latency budget on target hardware.