Extrinsic Self Calibration

Extrinsic self-calibration focuses on automatically determining the relative spatial positions and orientations of multiple sensors on a platform (e.g., cameras, LiDAR, IMUs) without relying on external calibration targets. Current research emphasizes robust and efficient algorithms, often leveraging geometric features from sensor data (e.g., point clouds, image edges) or incorporating self-supervised learning techniques to estimate these parameters. This capability is crucial for improving the accuracy and reliability of sensor fusion in applications like autonomous vehicles and robotics, enabling more robust perception and navigation.