Robotics Sensor Fusion Guide | Guide to Sensor Fusion Techniques, Calibration, and Data Processing

Common techniques include: Kalman filtering (optimal for Gaussian noise), particle filtering (handles non-Gaussian, nonlinear), complementary filtering (simple, efficient), and Bayesian fusion (principled, uncertainty quantification). Technique selection depends on sensor characteristics, system requirements, and computational constraints.

Sensor calibration involves several steps, including intrinsic calibration (determining internal sensor parameters), extrinsic calibration (establishing the relative positions and orientations between sensors), and temporal calibration (synchronizing sensor timestamps). Accurate calibration is essential for effective sensor fusion.

Calibration encompasses intrinsic calibration, which focuses on internal sensor parameters, extrinsic calibration, which determines the relative pose between sensors, and temporal calibration, which synchronizes sensor timestamps to account for variations in sampling rates or delays.

Commonly fused sensors in robotics include cameras (for visual data), LiDAR (for 3D mapping), IMUs (inertial measurement units providing acceleration and angular rate measurements), and ultrasonic sensors (for proximity detection).