Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Xuesong Shi
  • Dongjiang Li
  • Pengpeng Zhao
  • Qinbin Tian
  • Yuxin Tian
  • Qiwei Long
  • Chunhao Zhu
  • Jingwei Song
  • Fei Qiao
  • Le Song
  • Yangquan Guo
  • Zhigang Wang
  • Yimin Zhang
  • Baoxing Qin
  • Wei Yang
  • Fangshi Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages3139-3145
ISBN (Electronic)9781728173955
ISBN (Print)9781728173962
Publication statusPublished - May 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Title2020 IEEE International Conference on Robotics and Automation (ICRA 2020)
LocationVirtual
PlaceFrance
CityParis
Period31 May - 31 August 2020

Abstract

Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may be significantly different in some successive days. Such out-of-sight dynamics pose great challenges to the robustness of pose estimation, and hence a robot's long-term deployment and operation. To differentiate the forementioned problem from the conventional works which are usually evaluated in a static setting in a single run, the term lifelong SLAM is used here to address SLAM problems in an ever-changing environment over a long period of time. To accelerate lifelong SLAM research, we release the OpenLORIS-Scene datasets. The data are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life. We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately. The datasets and benchmark are available online at lifelong-robotic-vision.github.io/dataset/scene.

Citation Format(s)

Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM. / Shi, Xuesong; Li, Dongjiang; Zhao, Pengpeng; Tian, Qinbin; Tian, Yuxin; Long, Qiwei; Zhu, Chunhao; Song, Jingwei; Qiao, Fei; Song, Le; Guo, Yangquan; Wang, Zhigang; Zhang, Yimin; Qin, Baoxing; Yang, Wei; Wang, Fangshi; Chan, Rosa H. M.; She, Qi.

2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. p. 3139-3145 9196638 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review