GPU-Accelerated 3D Normal Distributions Transform

被引：0

作者：

Nguyen, Anh ^{[1
]}

Cano, Abraham Monrroy ^{[2
]}

Edahiro, Masato ^{[1
]}

Kato, Shinpei ^{[3
]}

机构：

[1] Nagoya Univ, Grad Sch Informat, Furo Cho,Chikusa Ku, Nagoya 4648603, Japan

[2] Nagoya Univ, MAP IV Inc, Natl Innovat Complex 711,Furo Cho, Nagoya, Aichi 4640814, Japan

[3] Univ Tokyo, Grad Sch Informat Sci & Technol, Dept Comp Sci, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138656, Japan

来源：

JOURNAL OF ROBOTICS AND MECHATRONICS | 2023年 / 35卷 / 02期

关键词：

3D normal distributions transform; GPGPU; point cloud; autonomous driving systems; SLAM; SCAN REGISTRATION;

D O I：

10.20965/jrm.2023.p0445

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

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 appli-cations. 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 investi-gated the use of NVIDIA's GPUs and their program-ming platform called compute unified device architec-ture (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).

引用

页码：445 / 459

页数：15

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