Binary Feature Description of 3D Point Cloud Based on Retina-like Sampling on Projection Planes

Abstract

A binary feature description and registration algorithm for a 3D point cloud based on retina-like sampling on projection planes (RSPP) are proposed in this paper. The algorithm first projects the point cloud within the support radius around the key point to the XY, YZ, and XZ planes of the Local Reference Frame (LRF) and performs retina-like sampling on the projection plane. Then, the binarized Gaussian density weight values at the sampling points are calculated and encoded to obtain the RSPP descriptor. Finally, rough registration of point clouds is performed based on the RSPP descriptor, and the RANSAC algorithm is used to optimize the registration results. The performance of the proposed algorithm is tested on public point cloud datasets. The test results show that the RSPP-based point cloud registration algorithm has a good registration effect under no noise, 0.25 mr, and 0.5 mr Gaussian noise. The experimental results verify the correctness and robustness of the proposed registration method, which can provide theoretical and technical support for the 3D point cloud registration application.