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《Science of Surveying and Mapping》 2019-01
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Using rigidity and normal consistency based sample consensus for 3Dpoint cloud registration

ZHANG Qian;LI Mengyao;CHENG Xiaoqiang;Faculty of Resources and Environmental Science,Hubei University;Hubei Key Laboratory of Regional Development and Environmental Response;  
Considering the large amount of false matches and low efficiency problems exist in the feature matching stage of 3 Dpoint cloud registration methods,this paper proposes a sample consensus algorithm based on rigidity and normal consistency.Traditional random sample consensus(RANSAC)suffers from the requirement of huge iterations and low precision problems,we solve these problems by improving the sampling strategy,first,we consider normal information during sampling so as to reduce the sample size from 3 to 2;second,we test the rigidity and normal consistency of current sampled matches to judge if they are convincing;finally,samples yield to the largest number of inliers are selected for transformation estimation in our iterative algorithm.Experiments on LiDAR point clouds show that the proposed algorithm exceeds RANSAC in terms of both precision and efficiency.
【Fund】: 湖北省教育厅青年项目(Q20171002)
【CateGory Index】: P225.2
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