Versatile Multi-LiDAR Accurate Self-Calibration System Based on Pose Graph Optimization

Multi-LiDAR systems provide all-round perception of the surrounding environment from different directions simultaneously, significantly enhancing the sensing range and density of measurements. Accurate extrinsic calibration is a key prerequisite for data fusion of multi-LiDAR systems. However, existing multi-LiDAR calibration methods mostly rely on calibration targets, priori environmental information, and initial calibration values, resulting in complex calibration processes and poor adaptability to environments. To this end, this letter proposes a targetless multi-LiDAR automatic calibration method based on global pose graph optimization, which does not rely on field-of-view (FoV) overlaps and can be applied to multiple LiDARs. First, LiDAR odometry is employed to estimate poses of each LiDAR, and relative motion constraints are used to recover initial extrinsic parameters. Then, absolute poses of each LiDAR are estimated by scan-map matching and used as absolute motion constraints for global pose graph to refine calibration parameters, which not only satisfies the observability of the 6 degree-of-freedom (DoF) calibration parameter space, but also avoids the influence of pose estimation error on the calibration accuracy. In addition, global optimization improves the global consistency of multi-LiDAR systems compared to separate pairwise parameter estimation. The proposed multi-LiDAR calibration method has been tested on various datasets. The results of performance evaluation and ablation studies have verified the reliability, accuracy and versatility of our method.