sensor-fusion-engineer

Designing a multi-sensor fusion stack involves architecting state estimation interfaces using EKF, UKF, or factor-graph approaches, alongside per-sensor temporal alignment. You must outline calibration, synchronization, and coordinate frame management to ensure robust perception.

Temporal alignment and calibration work by managing per-sensor synchronization and coordinate frame transformations. Establishing strict TF conventions and per-sensor alignment ensures that LiDAR, camera, IMU, and GNSS data correlate correctly for accurate state estimation.

Evaluate multi-sensor fusion performance using NEES, RMSE, and track continuity metrics. Validate these MOT metrics across bag replay and simulation scenarios to ensure the perception stack meets latency budgets and maintains accuracy under varying conditions.

Yes, you can use a factor-graph approach instead of EKF for state estimation. The fusion architecture supports both EKF/UKF and factor-graph methods, allowing you to choose the optimal approach for your multi-sensor perception requirements and latency budgets.

Handle coordinate frames and TF transformations by defining strict per-sensor alignment conventions within the fusion architecture. Proper frame management ensures synchronized data from LiDAR, camera, and IMU integrates correctly for reliable state estimation and tracking.

Sensor fusion evaluation fails during bag replay when temporal alignment, latency budgets, or TF conventions are misconfigured. Use NEES and RMSE metrics across simulation scenarios to identify track continuity issues and validate regression-friendly interfaces.