Point Cloud Registration Method Based on Fusion of Corner Detection
In order to improve the registration efficiency of high point cloud,a Harris corner extraction al-gorithm based on the curvature information of point cloud is proposed.By introducing curvature con-straints,the traditional Harris corner extraction is improved.The curvature value of each point is calculated and compared with the average curvature of the whole point cloud.The point clouds whose curvature value is greater than the average curvature are retained to eliminate the points with low registration value and re-duce the calculation amount for subsequent point cloud registration.Construct feature matching between ex-tracted corner points,and combine sample consensus initial alignment(SAC-IA)and normal distributions transform(NDT)with fine registration.Find the optimal transformation matrix to achieve the coincidence of source point cloud and target point cloud.By comparing different algorithms on public data sets,the ex-perimental results show that this method has good performance on complex point clouds and multi-inflection point clouds,and also has good applicability to point clouds with large initial position difference.
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