Point cloud registration method based on 3DHarris-FPFH features
In view of the problem that the traditional ICP algorithm has a long registration time and tends to fall into the local optimum when the initial positions of the two-point clouds differ greatly,a point cloud registration method based on the feature improvement of 3DHarris key points combined with fast point feature histogram is proposed to improve the point cloud alignment.Firstly,the input point cloud is streamlined using voxel down sampling,and then the 3DHarris al-gorithm is applied to extract key points from the streamlined two-piece point cloud,and the 3DHarris-FPFH feature points are formed by the FPFH,and then the Random Sample Consensus(RANSAC)algorithm is used to coarsely align and output the initial transformation matrix.Finally,the refined alignment is performed by the improved Iterative Closest Point(ICP)algorithm.The algorithm is simulated on open data set,and the results show that the algorithm can improve the operation speed while maintaining the accuracy,and has certain practicability.
3DHarris key pointsfast point feature histogram(FPFH)iteration closest point(ICP)point cloud registration
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