基于云的移动机器人SLAM并行实现

Supun Kamburugamuve, Hengjing He, G. Fox, David J. Crandall
{"title":"基于云的移动机器人SLAM并行实现","authors":"Supun Kamburugamuve, Hengjing He, G. Fox, David J. Crandall","doi":"10.1145/2896387.2896433","DOIUrl":null,"url":null,"abstract":"Simultaneous Localization and Mapping (SLAM) for mobile robots is a computationally expensive task. A robot capable of SLAM needs a powerful onboard computer, but this can limit the robot's mobility because of weight and power demands. We consider moving this task to a remote compute cloud, by proposing a general cloud-based architecture for real-time robotics computation, and then implementing a Rao-Blackwellized Particle Filtering-based SLAM algorithm in a multi-node cluster in the cloud. In our implementation, expensive computations are executed in parallel, yielding significant improvements in computation time. This allows the algorithm to increase the complexity and frequency of calculations, enhancing the accuracy of the resulting map while freeing the robot's onboard computer for other tasks. Our method for implementing particle filtering in the cloud is not specific to SLAM and can be applied to other computationally-intensive tasks.","PeriodicalId":342210,"journal":{"name":"Proceedings of the International Conference on Internet of things and Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Cloud-based Parallel Implementation of SLAM for Mobile Robots\",\"authors\":\"Supun Kamburugamuve, Hengjing He, G. Fox, David J. Crandall\",\"doi\":\"10.1145/2896387.2896433\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Simultaneous Localization and Mapping (SLAM) for mobile robots is a computationally expensive task. A robot capable of SLAM needs a powerful onboard computer, but this can limit the robot's mobility because of weight and power demands. We consider moving this task to a remote compute cloud, by proposing a general cloud-based architecture for real-time robotics computation, and then implementing a Rao-Blackwellized Particle Filtering-based SLAM algorithm in a multi-node cluster in the cloud. In our implementation, expensive computations are executed in parallel, yielding significant improvements in computation time. This allows the algorithm to increase the complexity and frequency of calculations, enhancing the accuracy of the resulting map while freeing the robot's onboard computer for other tasks. Our method for implementing particle filtering in the cloud is not specific to SLAM and can be applied to other computationally-intensive tasks.\",\"PeriodicalId\":342210,\"journal\":{\"name\":\"Proceedings of the International Conference on Internet of things and Cloud Computing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the International Conference on Internet of things and Cloud Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2896387.2896433\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on Internet of things and Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2896387.2896433","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

摘要

移动机器人的同步定位和映射(SLAM)是一项计算成本很高的任务。一个能够SLAM的机器人需要一个强大的机载计算机,但这可能会限制机器人的机动性,因为重量和功率需求。我们考虑将这项任务转移到远程计算云,通过提出一个通用的基于云的实时机器人计算架构,然后在云中的多节点集群中实现基于rao - blackwell化粒子滤波的SLAM算法。在我们的实现中,昂贵的计算是并行执行的,从而显著改善了计算时间。这使得算法可以增加计算的复杂性和频率,提高结果地图的准确性,同时释放机器人的车载计算机用于其他任务。我们在云中实现粒子过滤的方法并不特定于SLAM,可以应用于其他计算密集型任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud-based Parallel Implementation of SLAM for Mobile Robots
Simultaneous Localization and Mapping (SLAM) for mobile robots is a computationally expensive task. A robot capable of SLAM needs a powerful onboard computer, but this can limit the robot's mobility because of weight and power demands. We consider moving this task to a remote compute cloud, by proposing a general cloud-based architecture for real-time robotics computation, and then implementing a Rao-Blackwellized Particle Filtering-based SLAM algorithm in a multi-node cluster in the cloud. In our implementation, expensive computations are executed in parallel, yielding significant improvements in computation time. This allows the algorithm to increase the complexity and frequency of calculations, enhancing the accuracy of the resulting map while freeing the robot's onboard computer for other tasks. Our method for implementing particle filtering in the cloud is not specific to SLAM and can be applied to other computationally-intensive tasks.
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