Voxelized GICP for Fast and Accurate 3D Point Cloud Registration

This paper presents 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.

Automated summary

This paper introduces the voxelized generalized iterative closest point (VGICP) algorithm for fast and accurate 3D point cloud registration. By extending the GICP approach with voxelization, the proposed method avoids costly nearest neighbor searches and allows for efficient processing on both CPU and GPU. Evaluations in simulated and real environments have shown that the algorithm is comparable in accuracy to GICP but significantly faster, enabling real-time 3D LIDAR applications.