RANSAC-ICP 3D point cloud registration method based on feature point extraction
Aiming at solving the problems of mismatch,long iteration time and low accuracy in data registration,this paper proposed a random sample consensus-iterative closest point registration method based on feature point registration.The algorithm framework consisted of voxel filtering down-sampling,key feature point extraction and geometric feature description,improved random sample consensus and point-to-plane iterative closest point iteration.Based on downsampling,the key geometric feature points of the point cloud were extracted and the key point neighborhood description was performed.We used the random sample consensus algorithm for four-point pairs and the point-to-plane iterative closest point algorithm to realize the point cloud coarse alignment and fine alignment.LiDAR and supporting equipment were used to simulate unmanned vehicles to collect point cloud data.According to the experimental data,the point cloud images of different time intervals were selected,and the root mean square error and the operation time index were introduced to verify the performance of the algorithm.In terms of coarse registration,the registration speed was improved by 78.44%and 61.02%compared with sample consensus initial alignment and 4-Points Congruent Sets algorithms,and the registration error below 100 frames was within 10 cm.In terms of fine registration,the registration error was 5.11 cm,4.94 cm and 0.53 cm lower than that of coarse registration,normal distributions transform algorithm and traditional iterative closest point algorithm respectively,and the registration time was 33.06%higher than that of traditional iterative closest point algorithm.
Laser SLAMpoint cloud registrationkey point extractionrandom sample consensusiterative closest point
万方数据
维普
