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JRM Vol.35 No.2 pp. 445-459
doi: 10.20965/jrm.2023.p0445
(2023)

Paper:

GPU-Accelerated 3D Normal Distributions Transform

Anh Nguyen*, Abraham Monrroy Cano** ORCID Icon, Masato Edahiro* ORCID Icon, and Shinpei Kato*** ORCID Icon

*Graduate School of Informatics, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

**MAP IV, Inc.
711 National Innovation Complex, Nagoya University, Furo-cho, Nagoya, Aichi 464-0814, Japan

***Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:
October 28, 2022
Accepted:
November 29, 2022
Published:
April 20, 2023
Keywords:
3D normal distributions transform, GPGPU, point cloud, autonomous driving systems, SLAM
Abstract

The three-dimensional (3D) normal distributions transform (NDT) is a popular scan registration method for 3D point cloud datasets. It has been widely used in sensor-based localization and mapping applications. However, the NDT cannot entirely utilize the computing power of modern many-core processors, such as graphics processing units (GPUs), because of the NDT’s linear nature. In this study, we investigated the use of NVIDIA’s GPUs and their programming platform called compute unified device architecture (CUDA) to accelerate the NDT algorithm. We proposed a design and implementation of our GPU-accelerated 3D NDT (GPU NDT). Our methods can achieve a speedup rate of up to 34 times, compared with the NDT implemented in the point cloud library (PCL).

Mapping using 3D NDT

Mapping using 3D NDT

Cite this article as:
A. Nguyen, A. Cano, M. Edahiro, and S. Kato, “GPU-Accelerated 3D Normal Distributions Transform,” J. Robot. Mechatron., Vol.35 No.2, pp. 445-459, 2023.
Data files:
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Last updated on May. 19, 2024