Perception

Sensor Fusion

Sensor fusion is the combination of measurements from multiple sensors into a single, more accurate and reliable estimate than any individual source provides. Classical robotics fuses IMU, wheel odometry, LiDAR, GPS, and cameras through probabilistic estimators such as Kalman filters and factor graph optimization; visual-inertial odometry is a canonical example. In learned systems, fusion also happens inside networks that ingest multimodal streams like RGB, depth, proprioception, and touch.

Why it matters for physical AI

No single sensor survives all conditions — cameras fail in darkness, IMUs drift, LiDAR struggles with glass — so principled fusion underwrites the state estimates every controller depends on.