gpu加速3D正态分布变换

A. Nguyen, Abraham Monrroy Cano, M. Edahiro, Shinpei Kato
{"title":"gpu加速3D正态分布变换","authors":"A. Nguyen, Abraham Monrroy Cano, M. Edahiro, Shinpei Kato","doi":"10.20965/jrm.2023.p0445","DOIUrl":null,"url":null,"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).","PeriodicalId":178614,"journal":{"name":"J. Robotics Mechatronics","volume":"729 1-2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GPU-Accelerated 3D Normal Distributions Transform\",\"authors\":\"A. Nguyen, Abraham Monrroy Cano, M. Edahiro, Shinpei Kato\",\"doi\":\"10.20965/jrm.2023.p0445\",\"DOIUrl\":null,\"url\":null,\"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).\",\"PeriodicalId\":178614,\"journal\":{\"name\":\"J. Robotics Mechatronics\",\"volume\":\"729 1-2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Robotics Mechatronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20965/jrm.2023.p0445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Robotics Mechatronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20965/jrm.2023.p0445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

三维正态分布变换(NDT)是一种常用的三维点云数据扫描配准方法。它已广泛应用于基于传感器的定位和地图应用。然而,由于无损检测的线性特性,它不能完全利用现代多核处理器(如图形处理单元(gpu))的计算能力。在本研究中,我们研究了使用NVIDIA的gpu及其称为计算统一设备架构(CUDA)的编程平台来加速NDT算法。我们提出了一种GPU加速3D无损检测(GPU NDT)的设计和实现。与在点云库(PCL)中实现的无损检测相比,我们的方法可以实现高达34倍的加速率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GPU-Accelerated 3D Normal Distributions Transform
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).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信