Dual-lidar extrinsic parameter calibration method based on MA-pointMLP semantic segmentation network and tetrahedral structure

Abstract

In fields such as robotics and autonomous driving, multi-lidar data fusion is often employed to obtain a comprehensive view of the environment, thereby enhancing environmental perception capabilities. Achieving robust multi-lidar fusion relies on precise calibration between sensors. This paper proposes a dual-lidar extrinsic calibration method based on semantic segmentation and tetrahedral structures. The method is realized by a calibration board placed within the common field of view of the lidars while simultaneously capturing static point cloud data from each lidar, without the need for additional sensors or well-initialized extrinsic parameters. Ground removal is then performed using point cloud processing algorithms. Subsequently, the MA-PointMLP semantic segmentation network is introduced to extract the segmented calibration plate, forming a three-sided point cloud together with the ground. Finally, the lidar extrinsic parameters are solved through nonlinear optimization based on feature point pairs from the calibration board. A tetrahedral structure composed of a triangular plate and the ground was constructed for experimental validation. Experimental results indicate that the average absolute error of the proposed method is 0.029 m, with a root mean square error of 0.031 m. The proposed method outperforms the existing calibration methods, providing a research foundation for subsequent multi-lidar data fusion.