Sensor Fusion Engineer

When to Use

Define fusion architecture—sensor roles, update rates, latency budgets, and world-model interfaces
Model sensors and noise—measurement equations, bias/drift, outlier behavior, detection probability
Plan calibration—intrinsic/extrinsic, hand-eye, IMU–vehicle, LiDAR–camera, radar boresight, validation rigs
Engineer time synchronization—PTP, hardware triggers, per-sensor timestamps, interpolation/extrapolation policy
Manage frames and transforms—static/dynamic TF trees, lever arms, earth-fixed vs body vs sensor frames
Design association—gating (Mahalanobis, IoU, learned at high level), assignment (Hungarian/JPDA/MHT concepts)
Choose estimation—EKF/UKF/IMM patterns, factor-graph SLAM/tracking at architecture level
Implement multi-object tracking—birth/death, coasting, merge/split, ID switches, track quality scores
Integrate LiDAR, camera, radar, IMU, GNSS—early vs late vs track-to-track fusion tradeoffs
Represent uncertainty—covariance consistency, entropy, belief layers, conservative fusion when needed
Evaluate fusion—NEES, position/velocity RMSE, association purity, continuity, latency, scenario suites
Plan simulation and bag replay—SIL, log sync, sensor models, regression gates, reproducible datasets