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

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

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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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Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385773 · Cited by 4,230
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094
Direct Sparse Odometry
Engel, J. J., Koltun, V., & Cremers, D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2658577 · Cited by 2,995
Least-squares estimation of transformation parameters between two point patterns
Umeyama, S. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.88573 · Cited by 2,429
The EuRoC micro aerial vehicle datasets
Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M., & Siegwart, R. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915620033 · Cited by 2,086
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
Yu, C., Liu, Z., Liu, X.-J., Xie, F., Yang, Y., Wei, Q., & Fei, Q. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593691 · Cited by 1,154

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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: Shi, Xuesong
Li, Dongjiang
Zhao, Pengpeng
Tian, Yuxin
Long, Qiwei
Song, Jingwei
Qiao, Fei
Song, Le
Zhang, Yimin
Qin, Baoxing
Yang, Wei
Chan, Rosa H. M.
She, Qi
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: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

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