Super-4PCS Point Cloud Alignment Method Combined with Improved FPFH
In response to the problems of high computational complexity and long alignment time in the coarse matching process of the Super-4 Points Fast Robust Matching Algorithm(Super-4PCS),a Super-4PCS coarse alignment algorithm combined with the improved Fast Point Feature Histogram(FPFH)is proposed.The feature points that can represent the feature information of the point cloud are filtered from the fast point feature histogram by principal component analysis(PCA),and the filtered feature point cloud is used as the input data for Super-4PCS coarse alignment,and the initial transformation matrix is obtained from Super-4PCS coarse alignment,and then the nearest point iteration algorithm(ICP)fine alignment is further performed.Two points cloud data-sets with different densities,Bunny and Dragon,are used for the alignment experiments to verify the matching efficiency under dif-ferent densities of point clouds.Based on satisfying the fine alignment accuracy,compared with the FPFH-SAC and Super-4PCS coarse alignment methods,the coarse alignment rate is improved by 72%and 58%respectively,and the overall alignment rate is improved by 43%and 32%respectively.
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