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 accessAbstract
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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“Lifelong SLAM emphasizes the positioning failure and mismatch problems caused by scene changes, which could be addressed by incremental learning.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [137]
Citations
Cites
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898 · cited 2×
“Then ATE of each matched ORB_SLAM2 [16] (stereo fisheye) ORB_SLAM2 [16] (RGB-D) DS-SLAM [18] (RGB-D) DSO [17] (RGB) VINS-Mono [19] (RGB+IMU) VINS-Mono [19] (fisheye+IMU) InfiniTAMv2 [20] (RGB-D) ElasticFusion [ The percentage value on the top left of each scene is average Correct Rate (CR ε , φ as…”
BackgroundVINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669
A benchmark for the evaluation of RGB-D SLAM systems
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
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
Cited by
Sorted by citations
M2DGR: A Multi-Sensor and Multi-Scenario SLAM Dataset for Ground Robots
Yin, J., Li, A., Li, T., Yu, W., & Zou, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3138527 · Cited by 304
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
Li, D., Shi, X., Long, Q., Liu, S., Yang, W., Wang, F.-S., Wei, Q., & Qiao, F. (2020) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros45743.2020.9340907 · Cited by 161
Self-citationSG-SLAM: A Real-Time RGB-D Visual SLAM Toward Dynamic Scenes With Semantic and Geometric Information
Cheng, S., Sun, C., Zhang, S., & Zhang, D. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3228006 · Cited by 144
An Appraisal of Incremental Learning Methods
Luo, Y., Yin, L., Bai, W., & Mao, K. (2020) · Entropy · doi:10.3390/e22111190 · Cited by 120
Visual SLAM: What Are the Current Trends and What to Expect?
Tourani, A., Bavle, H., Sánchez-López, J. L., & Voos, H. (2022) · Sensors · doi:10.3390/s22239297 · Cited by 133
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)
- 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
- 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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