## 3 Point Cloud Registration Method
### ICP-Based Variants  
The ICP algorithm has some shortcomings, such as sensitivity to the initial value [38], high computational overhead [39], being easily trapped in a local optimal solution [40], and a lack of utilization of point cloud structure information [41]. In response to these problems, experts and scholars have exploited many improvement methods. [PERSON] [42] proposed the PLICP algorithm, using the point-line distance metric to replace the point-to-point distance metric in the ICP algorithm. Compared with P2P-ICP, PLICP has higher accuracy and requires fewer iterations, but PLICP is more susceptible to initial values. [PERSON] and [PERSON] [43] proposed the P2 PL-ICP algorithm, which improved the selection method of the corresponding points in the ICP algorithm (P2P-ICP), and used the distance from the point to the surface instead of the distance from the point to the point to improve the convergence speed. [PERSON] et al. [44] presented the Generalized-ICP (GICP) algorithm, which combines the P2P-ICP algorithm and the P2 PL-ICP algorithm into a probabilistic framework based on which point cloud matching is carried out. The covariance matrix plays a role analogous to the weight to eliminate some bad corresponding points in the solution process. The GICP algorithm can flexibly model different models, the P2P-ICP algorithm is equivalent to a unique form of the GICP algorithm. GICP algorithm can effectively reduce the impact of mismatch, and it constitutes one of the most effective and robust algorithms among many improved ICP algorithms. Based on GICP, [PERSON] et al. [45] extended the GICP method by voxelization to improve the registration speed while maintaining its accuracy. In addition, the multi-threaded CPU GICP algorithm is implemented. [PERSON] et al. [46] proposed the ICP algorithm using invariant features (ICPIF) by combining Euclidean invariant features with the ICP algorithm. The algorithm uses the distance function of the weighted linear combination of position distance and feature distance to obtain the corresponding relationship. The experimental results demonstrate that the intersection is more accurate than the corresponding relationship using only position distance. [PERSON] et al. [47] used a new distance scale function to consider both translation and rotation. Compared with the ICP algorithm, the MblCP algorithm has improved robustness, accuracy, convergence, and computational load. [PERSON] and [PERSON] [48] used surface features such as the normal vector and curvature to filter out error points during matching. In the iterative solution process, the error function not only includes the projection distance of the normal vector between point clouds (same P2 PL-ICP) but also includes the normal vector direction error. Experimental analysis shows that the NICP algorithm has better accuracy and robustness. [PERSON] and [PERSON] [49] developed the EM-ICP algorithm and applied the EM algorithm to the ICP algorithm to avoid the initial registration step. Aiming at the point cloud distortion caused by motion (especially high-speed motion). [PERSON] et al. [50] introduced speed updates based on the ICP algorithm to obtain more accurate pose estimation. According to the distortion effect caused by sensor motion during point cloud registration, [PERSON] et al. [51] designed a Continuous-ICP (CICP) algorithm, which uses the estimated continuous 6 DOF pose of the sensor to correct the distortion and improves the accuracy and robustness of the algorithm. The ICP algorithm is easy to converge to the local minimum. To solve this problem, [PERSON] et al. [52] combined the ICP algorithm with the branch-and-bound method to ensure a global optimal solution. Although this method solves the local minimum problem, it is always sensitive to initialization and has a large computational cost. As to the challenge of accurate registration of outdoor large-scale multi-robot environment data fusion, [PERSON] et al.