Element 3d 2.2 auto-normals7/2/2023 ![]() Experimental results show that given a large amount of point cloud data, this algorithm has advantages in both time and precision. In the precise registration phase, the algorithm uses an improved normal distribution transformation (INDT) algorithm. In the registration process, FPFH features and Hausdorff distance are used to search for corresponding point pairs, and the RANSAC algorithm is used to eliminate incorrect point pairs, thereby improving the accuracy of the corresponding relationship. In the rough registration stage, the algorithm extracts feature points based on the judgment of retention points and bumps, which improves the speed of feature point extraction. ![]() ![]() In this paper, we propose a point cloud registration algorithm based on feature extraction and matching the algorithm helps alleviate problems of precision and speed. The existing registration algorithms suffer from low precision and slow speed when registering a large amount of point cloud data.
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