Voxelized GICP for Fast and Accurate 3D Point Cloud Registration

被引：217

作者：

Koide, Kenji ^{[1
]}

Yokozuka, Masashi ^{[1
]}

Oishi, Shuji ^{[1
]}

Banno, Atsuhiko ^{[1
]}

机构：

[1] Natl Inst Adv Ind Sci & Technol, Dept Informat Technol & Human Factors, Umezono 1-1-1, Tsukuba, Ibaraki 3050061, Japan

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) | 2021年

关键词：

HISTOGRAMS;

D O I：

10.1109/ICRA48506.2021.9560835

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：11054 / 11059

页数：6

共 19 条

[1]

Bai X., 2020, IEEE CVF C COMP VIS

[2] MULTIDIMENSIONAL BINARY SEARCH TREES USED FOR ASSOCIATIVE SEARCHING [J].