In response to the high initial value requirement for rapid registration of point clouds in large-scale scenarios,the traditional iterative closest point (ICP) registration algorithm was improved by using the spatial position of the measuring station and three-dimensional (3D) corner point features as spatial constraint information. By considering the relationship between point cloud density and the distance of the scanner,point-to-point criteria and point-to-line criteria were both used for precise point cloud registration. The example verification results show that in a scenario with 45.07 million point clouds,the root mean square error (RMSE) reaches 0.2311 m,which is 0.3521 m higher than that using the ICP algorithm directly and 0.1193 m higher than that using the random sample consensus+ICP (RANSAC+ICP) method. The time is shortened by 5.87 s and 18.32 s,respectively. In the scenario with 8.43 million point clouds,the RMSE reaches 0.0516 m,which is 1.0521 m higher than that using the ICP algorithm directly and 0.2669 m higher than that using the RANSAC+ICP method. The time is shortened by 2.10 s and 19.43 s,respectively. The extracted effective corner points have increased by 34.84％ compared to those by using the Harris 3D algorithm,proving that the proposed algorithm can be used for fast registration of scattered point clouds in large-scale scenarios.
关键词
大场景点云配准/地面三维激光扫描/三维角点特征/改进ICP算法
Key words
point cloud registration in large-scale scenarios/ground-based three-dimensional (3D) laser scanning/3D corner point feature/improved iterative closest point (ICP) algorithm
维普
万方数据