Local Feature Description of LiDAR Point Cloud Data Based on Hierarchical Mercator Projection

In order to efficiently extract the local geometric structure features of LiDAR point cloud data and realize the registration, detection and recognition of three-dimensional (3D) targets, a local point cloud feature descriptor based on hierarchical Mercator projection (HMcc) is proposed in this paper. First, the traditional method is used for feature extraction. Then, the local neighborhood points of 3D point cloud data arc projected onto multiple Mercator planes using the Mercator projection with conformal feature. Finally, the local feature descriptors of feature points arc obtained by counting the histogram of each Mercator plane. HMec feature descriptor can retain the local geometric structure features of point cloud, so as to improve the discrimination of feature descriptor. The test results on Bologna and 3DMatch datasets show that HMec feature descriptors have stronger discrimination and better noise robustness than the other nine local feature descriptors