D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI:10.1109/LRA.2023.3320011

Yun Chang;Luca Ballotta;Luca Carlone

{"title":"D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration","authors":"Yun Chang;Luca Ballotta;Luca Carlone","doi":"10.1109/LRA.2023.3320011","DOIUrl":null,"url":null,"abstract":"For a multi-robot team that collaboratively explores an unknown environment, it is of vital importance that the collected information is efficiently shared among robots in order to support exploration and navigation tasks. Practical constraints of wireless channels, such as limited bandwidth, urge robots to carefully select information to be transmitted. In this letter, we consider the case where environmental information is modeled using a \n<italic>3D Scene Graph</i>\n, a hierarchical map representation that describes both geometric and semantic aspects of the environment. Then, we leverage graph-theoretic tools, namely \n<italic>graph spanners</i>\n, to design greedy algorithms that efficiently compress 3D Scene Graphs with the aim of enabling communication between robots under bandwidth constraints. Our compression algorithms are \n<italic>navigation-oriented</i>\n in that they are designed to approximately preserve shortest paths between locations of interest while meeting a user-specified communication budget constraint. The effectiveness of the proposed algorithms is demonstrated in robot navigation experiments in a realistic simulator.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7527-7534"},"PeriodicalIF":4.6000,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7083369/10254630/10265226.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10265226/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}

引用次数: 0

Abstract

For a multi-robot team that collaboratively explores an unknown environment, it is of vital importance that the collected information is efficiently shared among robots in order to support exploration and navigation tasks. Practical constraints of wireless channels, such as limited bandwidth, urge robots to carefully select information to be transmitted. In this letter, we consider the case where environmental information is modeled using a 3D Scene Graph , a hierarchical map representation that describes both geometric and semantic aspects of the environment. Then, we leverage graph-theoretic tools, namely graph spanners , to design greedy algorithms that efficiently compress 3D Scene Graphs with the aim of enabling communication between robots under bandwidth constraints. Our compression algorithms are navigation-oriented in that they are designed to approximately preserve shortest paths between locations of interest while meeting a user-specified communication budget constraint. The effectiveness of the proposed algorithms is demonstrated in robot navigation experiments in a realistic simulator.

查看原文本刊更多论文

D-Lite：面向导航的多机器人协作三维场景图压缩

对于协作探索未知环境的多机器人团队来说，在机器人之间有效共享收集的信息以支持探索和导航任务至关重要。无线信道的实际限制，如有限的带宽，促使机器人仔细选择要传输的信息。在这封信中，我们考虑了使用3D场景图对环境信息进行建模的情况，3D场景图是一种描述环境的几何和语义方面的分层地图表示。然后，我们利用图论工具，即图扳手，设计贪婪算法，有效地压缩3D场景图，目的是在带宽限制下实现机器人之间的通信。我们的压缩算法是面向导航的，因为它们被设计为在满足用户指定的通信预算约束的同时，大致保持感兴趣位置之间的最短路径。在真实模拟器中的机器人导航实验中验证了所提出算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.