A Benchmark Dataset for Collaborative SLAM in Service Environments

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2024-11-04 DOI:10.1109/LRA.2024.3491415

Harin Park;Inha Lee;Minje Kim;Hyungyu Park;Kyungdon Joo

{"title":"A Benchmark Dataset for Collaborative SLAM in Service Environments","authors":"Harin Park;Inha Lee;Minje Kim;Hyungyu Park;Kyungdon Joo","doi":"10.1109/LRA.2024.3491415","DOIUrl":null,"url":null,"abstract":"We introduce a new multi-modal collaborative SLAM (C-SLAM) dataset for multiple service robots in various indoor service environments, called \n<monospace>C</monospace>\n-SLAM dataset in \n<monospace>S</monospace>\nervice \n<monospace>E</monospace>\nnvironments (\n<monospace>CSE</monospace>\n). We use the NVIDIA Isaac Sim to generate data in various indoor service environments with the challenges that may occur in real-world service environments. By using the simulator, we can provide precisely time-synchronized sensor data, such as stereo RGB/depth, IMU, and ground truth (GT) poses. We configure three common indoor service environments (\n<italic>Hospital</i>\n, \n<italic>Office</i>\n, and \n<italic>Warehouse</i>\n), each featuring dynamic objects performing motions suited to the environment. In addition, we drive the robots to mimic the actions of real service robots. Through these factors, we generate a realistic C-SLAM dataset for multiple service robots. We demonstrate our \n<monospace>CSE</monospace>\n dataset by evaluating diverse state-of-the-art single-robot SLAM and multi-robot SLAM methods. Additionally, we provide a detailed tutorial on generating C-SLAM data using the simulator.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10742554/","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

We introduce a new multi-modal collaborative SLAM (C-SLAM) dataset for multiple service robots in various indoor service environments, called C -SLAM dataset in S ervice E nvironments ( CSE ). We use the NVIDIA Isaac Sim to generate data in various indoor service environments with the challenges that may occur in real-world service environments. By using the simulator, we can provide precisely time-synchronized sensor data, such as stereo RGB/depth, IMU, and ground truth (GT) poses. We configure three common indoor service environments ( Hospital , Office , and Warehouse ), each featuring dynamic objects performing motions suited to the environment. In addition, we drive the robots to mimic the actions of real service robots. Through these factors, we generate a realistic C-SLAM dataset for multiple service robots. We demonstrate our CSE dataset by evaluating diverse state-of-the-art single-robot SLAM and multi-robot SLAM methods. Additionally, we provide a detailed tutorial on generating C-SLAM data using the simulator.

查看原文本刊更多论文

服务环境中协作式 SLAM 的基准数据集

我们介绍了一种新的多模式协作 SLAM（C-SLAM）数据集，用于在各种室内服务环境中使用多个服务机器人，称为服务环境中的 C-SLAM 数据集（CSE）。我们使用英伟达 Isaac Sim 在各种室内服务环境中生成数据，以应对真实世界服务环境中可能出现的挑战。通过使用模拟器，我们可以提供精确的时间同步传感器数据，如立体 RGB/深度、IMU 和地面实况（GT）姿势。我们配置了三种常见的室内服务环境（医院、办公室和仓库），每种环境中的动态物体都会做出与环境相适应的动作。此外，我们还模仿真实服务机器人的动作来驱动机器人。通过这些因素，我们为多个服务机器人生成了一个逼真的 C-SLAM 数据集。我们通过评估各种最先进的单机器人 SLAM 和多机器人 SLAM 方法来展示我们的 CSE 数据集。此外，我们还提供了使用模拟器生成 C-SLAM 数据的详细教程。

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.