Multi-source point cloud registration method based on automatically calculating overlap

In order to solve the problems (noise, partial overlap, registration parameters determining for different models, etc.) in multi-source point cloud registration, an improved TrICP algorithm based on the contribution factor was proposed. First, the improved voxel down-sampling and random down-sampling methods were adopted to resample the point cloud. The contribution factor was proposed to investigate the point pairs that contributed more to the registration. The transformation matrix was solved by using singular value decomposition. At the same time, slopes between points on the distance curve and the original point were used to calculate the overlap automatically. Therefore, the automatic registration of point cloud was realized. Comparative experiments among several registration algorithms were conducted based on the Stanford University Bunny point cloud and the 'Maoxian 624' landslide point cloud. The results show that the speeds of the improved algorithm on Bunny and landslide increase by 50 % and 67 % respectively and the accuracies are improved. In addition, it performs well even with a lot of noise. The improved algorithm can align the laser point cloud and point cloud from image reconstruction effectively and automatically, which contain lots of noise, partial overlap, non-homology. Then, the advantages of multi-source data are combined to obtain the accurate point cloud information of the target.