Three-dimensional point cloud registration technique for self-designed LiDAR

In order to realize the registration of point cloud data respectively obtained from LiDAR and camera, we used a fast multi-scale registration (FMSR) algorithm to register the point cloud data, based on a self-designed three-dimensional scanning laser radar system in our laboratory. The algorithm includes two steps: coarse registration and fine registration. In the coarse registration, an adaptive scale key point quality (ASKQ) algorithm was used to match key points and determine the initial parameters for fine registration. And in the fine registration, K-nearest neighbors (KNN) algorithm was used to simplify the search process and improve the algorithm efficiency. The optimal rotation matrix, translation vector and scale factor between two sets of point cloud data were obtained through many iterations. The simulation verified the stability of FMSR algorithm for multiscale registration. Simulation and experimental results show that the proposed algorithm successfully registers the point cloud data of the self-made LiDAR system and commercial camera. The root-mean-square error of the registration is 0.194 m and the execution time is 16.207 s, for a building with size of 20.30 m×7.85 m×26.56 m. Compared with an existing scale-iterative closest point (S-ICP) algorithm, the registration accuracy of the proposed algorithm is improved by 0.131 m, and the execution time is reduced by 30%. The proposed point cloud registration method can provide an algorithm basis for scene reconstruction and texture matching. © 2019, Editorial Board of JBUAA. All right reserved.