Voxelized GICP for Fast and Accurate 3D Point Cloud Registration

EasyChair Preprint 2703

Abstract

This paper proposes the voxelized generalized iterative closest point (VGICP) algorithm for fast and accurate three-dimensional point cloud registration. The proposed approach extends the generalized iterative closest point (GICP) approach with voxelization to avoid costly nearest neighbor search while retaining its accuracy. In contrast to the normal distributions transform (NDT), which calculates voxel distributions from point positions, we estimate voxel distributions by aggregating the distribution of each point in the voxel. The voxelization approach allows us to efficiently process the optimization in parallel, and the proposed algorithm can run at 30 Hz on a CPU and 120 Hz on a GPU. Through evaluations in simulated and real environments, we confirmed that the accuracy of the proposed algorithm is comparable to GICP, but is substantially faster than existing methods. This will enable the development of real-time 3D LIDAR applications that require extremely fast evaluations of the relative poses between LIDAR frames.

Keyphrases: GPU computing, Registration Method, gicp algorithm, point cloud, registration, scan matching, vgicp algorithm, voxelized gicp algorithm

@booklet{EasyChair:2703,
  author    = {Kenji Koide and Masashi Yokozuka and Shuji Oishi and Atsuhiko Banno},
  title     = {Voxelized GICP for Fast and Accurate 3D Point Cloud Registration},
  howpublished = {EasyChair Preprint 2703},
  year      = {EasyChair, 2020}}