Extrinsic Calibration

Extrinsic calibration focuses on determining the relative spatial transformations between different sensors in a multi-sensor system, such as cameras and LiDARs or multiple IMUs, enabling accurate data fusion. Current research emphasizes automated, target-free methods, often employing techniques like plane-based constraints, edge feature matching, deep learning (including transformers and consistency learning), and Gaussian Mixture Models for robust registration and optimization. Accurate extrinsic calibration is crucial for applications like autonomous driving, robotics, and visual-inertial odometry, improving the reliability and precision of environmental perception and navigation.