An Automatic Multi-LiDAR Extrinsic Calibration Algorithm Using Corner Planes

With the development of autonomous driving, more autonomous vehicles are equipped with multiple sensors to perform better. For multi-sensor systems, accurate extrinsic calibration is a prerequisite for perception and localization systems. Extrinsic calibration aims to transform two or more sensors' coordinates into a unified spatial coordinate system. Sensors usually need to be calibrated after installation to ensure accurate measurements. Most existing methods require special calibration markers or rely heavily on additional sensors to solve this problem. Recently, researchers have proposed a novel method that can calibrate extrinsic parameters using the common corners in surroundings to solve the multi-LiDARs extrinsic calibration problem, three linearly independent planar surfaces of the corner are utilized to compute the extrinsic parameters. However, this method is relatively inefficient and requires manual operation, especially in some hard scenes. To overcome this problem, we propose using the learning-based method to segment corners, improving efficiency and convenience without losing accuracy. By introducing learning-based corner segmentation method, we can achieve “one-click” extrinsic calibration, that is, there is no need to prepare auxiliary markers, there is no need to collect a large amount of data and data preprocessing, and the external parameters can be automatically calibrated.