Sensor Fusion 101: Combining IMU, Vision, and Force Data

No single sensor tells a robot everything it needs to know — the real picture comes from combining several imperfect ones.

Sensor fusion combines data from multiple sensors, each with different strengths and blind spots, into a single, more reliable estimate than any one sensor could provide alone.

A concrete example

Estimating a robot's exact position and velocity might combine IMU data (fast, drifts over time), camera-based visual odometry (accurate over short distances, can lose tracking in low-texture scenes), and leg odometry from joint encoders (reliable on solid ground, thrown off by slipping) — no single source is trustworthy in every situation, but together they cover each other's weaknesses.

Common techniques

Kalman filters and their variants (extended, unscented) are the classical workhorse for fusing continuous sensor streams with different noise characteristics. Increasingly, learned fusion approaches — neural networks trained to combine raw or partially processed sensor data end to end — are used alongside or instead of hand-designed filters, particularly for perception tasks like object recognition that don't reduce neatly to a filtering problem.