Point Cloud Registration Method Based on Improved RANSAC-ICP Algorithm
HUANG Liting
LIN Jingyu
LU Quan
Abstract：Aiming at the problems of low overlap rate,difficult to extract features and low registration accuracy in point cloud registration,a point cloud registration algorithm combining improved random sample consensus(RANSAC)algorithm and improved iterative nearest point(ICP)algorithm is proposed.Firstly,fast point feature histogram(FPFH)descriptor is used to describe the feature of point cloud.Secondly,by fusing geometric consistency and adopting the algorithm of improving random sampling consis-tency,the mismatched point pairs in the matching process are deleted in time to maintain the high-quality relationship between the corresponding points.The points with corresponding relationship can also be found under the point cloud with low overlap rate and the point cloud with noise for rough registration of point cloud.Finally,aiming at the problem that ICP registration takes a long time when the amount of point cloud data is large,KD-Tree search is used to orderly arrange the disordered point clouds for fine registra-tion.The real point cloud data scanned by lidar are used for experimental verification,and compared with the mainstream point cloud registration algorithm.The experimental results show that for the point cloud with low overlap rate and noise,the optimal trans-formation can be obtained quickly and accurately,and has a good registration effect.
Keywords：low overlappoint clouds registrationFPFHRANSACICP
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 2543-2548,2554 )