Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
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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 positioning failure and mismatch problems caused by scene changes, which could be addressed by incremental learning.
“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]
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Mahmoud, J., Penkovskiy, A., Vuong, H. T. L., Burkov, A., & Kolyubin, S. A. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342183 · Cited by 9 · see citing works
Wang, Y., Zhang, Y., Li-he, H., Wang, W., Ge, G., & Tan, S. (2023) · Sensors · doi:10.3390/s23208445 · Cited by 8 · see citing works
Self-citationSlimSLAM: An Adaptive Runtime for Visual-Inertial Simultaneous Localization and Mapping
Behroozi, A., Chen, Y., Fruchter, V., Subramanian, L., Srikanth, S., & Mahlke, S. A. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651361 · Cited by 8 · see citing works
NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM
Guo, W., Wang, B., & Chen, L. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3599100 · Cited by 8 · see citing works
Jia, Y., Wang, S., Shao, S., Wang, Y., Zhang, F., & Wang, T. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251401224 · Cited by 11 · see citing works
Dynamic visual SLAM based on probability screening and weighting for deep features
Fu, F., Yang, J., Ma, J., & Zhang, J. (2024) · Measurement · doi:10.1016/j.measurement.2024.115127 · Cited by 8 · see citing works
Object Aware RGBD SLAM in Dynamic Environments
Gokcen, B., & Uslu, E. (2022) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista55318.2022.9894245 · Cited by 8 · see citing works
Evaluation of RGB-D SLAM in Large Indoor Environments
Muravyev, K., & Yakovlev, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23609-9_9 · Cited by 8 · see citing works
IndoorMCD: A Benchmark for Low-Cost Multi-Camera SLAM in Indoor Environments
Sewtz, M., Fanger, Y., Luo, X., Bodenmüller, T., & Triebel, R. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3236840 · Cited by 8 · see citing works
SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware SLAM
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Self-citationSelf-Supervised Depth Completion for Active Stereo
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Fornasier, A., Scheiber, M., Hardt-Stremayr, A., Jung, R. T., & Weiß, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561382 · Cited by 7 · see citing works
A Benchmark Dataset for Collaborative SLAM in Service Environments
Park, H., Lee, I., Kim, M., Park, H., & Joo, K. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3491415 · Cited by 6 · see citing works
Improved Feature Point Extraction Method of VSLAM in Low-Light Dynamic Environment
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Self-citationDepth-Visual-Inertial (DVI) Mapping System for Robust Indoor 3D Reconstruction
Hamesse, C., Vlaminck, M., Luong, H. Q., & Haelterman, R. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3487496 · Cited by 6 · see citing works
SEESys: Online Pose Error Estimation System for Visual SLAM
Hu, T., Scargill, T., Yang, F., Chen, Y., Lan, G., & Gorlatova, M. A. (2024) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3666025.3699341 · Cited by 6 · see citing works
A Robust Visual SLAM System for Small-Scale Quadruped Robots in Dynamic Environments
Li, C., Zhang, Y., Yu, Z., Liu, X., & Shi, Q. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802163 · Cited by 6 · see citing works
Self-citationA Static Feature Point Extraction Algorithm for Visual-Inertial SLAM
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Robust Depth-Aided Visual-Inertial-Wheel Odometry for Mobile Robots
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Trekel, N., Guadagnino, T., Läbe, T., Wiesmann, L., Aguiar, P., Behley, J., & Stachniss, C. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246416 · Cited by 5 · see citing works
PRIOR-SLAM: Enabling Visual SLAM for Loop Closure Under Large Viewpoint Variations
Wang, Y., Bai, W., Zhang, Z., Wang, H., Sun, H. X., & Cao, Q. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3508193 · Cited by 5 · see citing works
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DynaPix SLAM: A Pixel-Based Dynamic Visual SLAM Approach
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Self-citationBS3D: Building-Scale 3D Reconstruction from RGB-D Images
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Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology
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Leveraging Local Planar Motion Property for Robust Visual Matching and Localization
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BDGS-SLAM: A Probabilistic 3D Gaussian Splatting Framework for Robust SLAM in Dynamic Environments
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Sigma-FP: Robot Mapping of 3D Floor Plans With an RGB-D Camera Under Uncertainty
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Multi-Feature Nonlinear Optimization Motion Estimation Based on RGB-D and Inertial Fusion
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Fast Extrinsic Calibration for Multiple Inertial Measurement Units in Visual-Inertial System
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Towards a Framework for SLAM Performance Investigation on Mobile Robots
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SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
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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
- 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 · 2019: 1
- 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 › Engineering › Aerospace Engineering › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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