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Cited by 185Robotics and Sensor-Based Localization3D Surveying and Cultural HeritageReinforcement Learning in Robotics

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.

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

Open access
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·2020·2020 IEEE International Conference on Robotics and Automation (ICRA)
DOI 10.1109/icra40945.2020.9196638
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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.

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Crossref · 10.1109/icra40945.2020.9196638

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Metadata

Authors
Shi, X., Li, D., Zhao, P., Tian, Q., Tian, Y., Long, Q., Zhu, C., Song, J., Qiao, F., Song, L., Guo, Y., Wang, Z., Zhang, Y., Qin, B., Yang, W., Wang, F., Chan, R. H. M., & She, Q.
Affiliations
  • Intel (United States) (US)
  • Tsinghua University (CN)
  • Gaussian Robotics,Shanghai,China,201203
  • Beijing Jiaotong University (CN)
  • City University of Hong Kong (HK)
ORCID
Venue
2020 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2020-05
Pages
3139-3145
ISSN
1050-4729 (ISSN-L)
DOI
10.1109/icra40945.2020.9196638
OpenAlex
W3089936780
Cited by
185
References
26
Citations by year
2026: 22 · 2025: 35 · 2024: 38 · 2023: 31 · 2022: 22 · 2021: 23 · 2020: 13
FWCI
32.80
Type
conference paper
Language
en
Open access
Licence
Locations
  • Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
  • Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
service robots, dynamic environments, scene change, indoor scenes, benchmarking metrics, visual SLAM

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