Sensor Fusion

Sensor fusion integrates data from multiple sources to create a single, high-confidence model of the environment that outperforms any individual sensor.

Modern perception systems rely on sensor fusion to bridge the gap between raw data and actionable intelligence. By combining high-frequency IMU motion estimates with absolute GPS positioning or overlaying 3D LiDAR point clouds onto 2D camera frames, engineers eliminate the blind spots inherent in standalone hardware. The process typically utilizes recursive algorithms like the Kalman Filter or Particle Filter to handle non-linear noise and temporal drift (common in robotics and ADAS). Whether it is a 9-axis IMU in a smartphone or a complex multi-modal stack in an autonomous vehicle, sensor fusion provides the redundancy and precision required for mission-critical decision making.

https://www.mathworks.com/products/sensor-fusion-and-tracking.html