Local voxelized structure for 3D binary feature representation and robust registration of point clouds from low-cost sensors

Abstract

Local feature-based 3D point cloud registration is a central issue in the fields of 3D computer vision and robotics, and most previously proposed 3D local features are real-valued. In this paper (1) a novel binary descriptor named local voxelized structure (LoVS) for 3D local shape description and (2) a LoVS-based registration algorithm for low-quality, e.g., Kinect-captured, point clouds are proposed. LoVS simply encodes the local shape structure represented by point clouds into bit string using point spatial locations without computing complex geometric attributes, such as normals and curvature, at the feature representation stage. Specifically, the LoVS descriptor is extracted within a local cubic volume around the keypoint. The orientation of the cubic volume is determined by a local reference frame (LRF) to achieve rotation invariance. Then, the cubic volume is uniformly split into a set of voxels. A voxel is labeled 1 if it contains points; otherwise, 0. All the labels are integrated into the LoVS descriptor. Based on the LoVS descriptor, a robust and accurate point cloud registration algorithm was developed, which effectively handles the challenges presented by low-cost sensors, e.g., noise and varying data resolutions. Experiments and extensive comparisons on three descriptor-matching benchmarks and a large-scale Kinect point cloud registration dataset show the effectiveness and the over-all superiority of our proposed LoVS descriptor and LoVS-based point cloud registration algorithm.