Sensor fusion

Sensor fusion is the process of combining data from multiple sensors to produce estimates that are more accurate, more reliable, or more complete than any single sensor could provide. It is a foundational technique in robotics, autonomous vehicles, and sensor networks, where no single sensor is sufficient for safe or effective operation.

The mathematics of sensor fusion is dominated by Bayesian inference and Kalman filter techniques, which treat each sensor as a noisy information source and combine their outputs according to their estimated reliability. A lidar sensor may provide precise depth measurements but fail in rain; a camera provides rich semantic information but poor depth; an IMU provides high-rate motion data but drifts over time. Sensor fusion is the architecture that integrates these partial, imperfect views into a coherent estimate.

The systems-theoretic significance of sensor fusion is that it demonstrates how reliable global behavior can emerge from unreliable local measurements. No sensor is trusted absolutely; each contributes according to its estimated precision, and the fusion algorithm dynamically reweights contributions as conditions change. This is a local update architecture applied to perception: each sensor updates locally, and the global estimate emerges from their interaction.