Coarse Point Cloud Registration Based on Adaptive Harris Corner Extraction

Aiming at the problems of the Harris operator using missing geometric information after dimension reducing, large response value calculation, huge time consumption, low matching accuracy of feature point pairs, and setting the corner response threshold manually, we propose a complete and efficient adaptive coarse point cloud registration method for corner description, extraction and matching. Firstly, the orthogonal gradient operator is introduced to improve the traditional Harris operator. Then, the point cloud curvature constraint is used to realize the adaptive screening and extraction of corner points, so as to make full use of the geometric information of the point cloud, reduce the calculation amount of the corner point response value, and improve the accuracy and efficiency of corner point extraction. Secondly, the feature descriptor based on intrinsic shape signatures is proposed to construct the corner geometric structure. Then combined with threshold detection and descriptor matching, the corner point matching is expanded to the set, so as to complete the coarse registration between the source point cloud and the target point cloud. Finally, the iterative closest point algorithm is used to realize the fine registration. The experimental results on the public data set show that: compared with five existing feature extraction and coarse point cloud registration algorithms, the proposed method can effectively mine more point cloud's local and global features, and obtain the best results. The correct corner matching rate of the proposed algorithm is 0.93, and the extraction time is 7.63 seconds. The proposed method combined with the fine registration step has the lowest rotation error, translation error and algorithm running time on the test data sets. © 2022, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.