ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Open accessAbstract
We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.
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Stochastic input sequences of edge-assisted SLAM cause a considerable impact on state dynamics and rewards, which incurs high variance during the training of policy gradient methods.
“As an input-driven system, the stochastic input sequences of edge-assisted SLAM cause a considerable impact on the state dynamics and rewards, which then incur high variance during the training of policy gradient methods [13], [14].”
From Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM (2025) · cites this paper as [14]
The Evaluation section (p. 6) mentions nondeterminism from multithreading and reports repeating each sequence to assess trajectory accuracy. Our copy does not address edge-assisted SLAM input sequences, state dynamics or rewards, or variance in policy-gradient training (searched for those terms and related phrasing). (LLM, conf=0.75)
Experimental — accuracy not yet measuredTraditional state-dependent DRL algorithms suffer from high state dynamics, leading to inherent bias in policy gradient estimates and increased gradient variance during training.
“In such an input-driven system, traditional state-dependent DRL algorithms suffer from high state dynamics, leading to inherent bias in policy gradient estimates and increased gradient variance during training [13], [14].”
From Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM (2025) · cites this paper as [14]
The ORB-SLAM2 section (p. 4) describes camera tracking, mapping, and loop closing; the bundle-adjustment section (p. 5) describes optimization of camera poses and map points. Searches for state-dependent DRL, policy-gradient bias, and gradient variance found no passage addressing the attributed claim. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Cites
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The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth
Ramezani, M., Wang, Y., Camurri, M., Wisth, D., Mattamala, M., & Fallon, M. F. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340849 · Cited by 248 · see citing works
Event-Based Stereo Visual Odometry
Zhou, Y. B., Gallego, G., & Shen, S. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3062252 · Cited by 241 · see citing works
Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation
Yang, X., Li, H., Zhai, H., Ming, Y., Liu, Y., & Zhang, G. (2022) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar55827.2022.00066 · Cited by 243 · see citing works
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
Yin, W., Zhang, C., Chen, H., Cai, Z., Yu, G., Wang, K., Chen, X., & Shen, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00830 · Cited by 238 · see citing works
An integrated UGV-UAV system for construction site data collection
Asadi, K., Suresh, A. K., Ender, A., Gotad, S., Maniyar, S., Anand, S., Noghabaei, M., Han, K. K., Lobaton, E. J., & Wu, T. (2020) · Automation in Construction · doi:10.1016/j.autcon.2019.103068 · Cited by 238 · see citing works
Unsupervised Traffic Accident Detection in First-Person Videos
Yao, Y., Xu, M., Wang, Y., Crandall, D., & Atkins, E. M. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967556 · Cited by 231 · see citing works
Learning the Depths of Moving People by Watching Frozen People
Li, Z., Dekel, T., Cole, F. H., Tucker, R. P., Snavely, N., Liu, C., & Freeman, W. T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00465 · Cited by 226 · see citing works
Limo: Lidar-monocular visual odometry
Graeter, J., Wilczynski, A., & Lauer, M. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594394 · Cited by 261 · see citing works
ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction
Zhao, X., Wu, X., Miao, J., Chen, W., Chen, P. C. Y., & Li, Z. G. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3155927 · Cited by 233 · see citing works
Huang, H., Li, L., Cheng, H., & Yeung, S.-K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02039 · Cited by 231 · see citing works
StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments
Scona, R., Jaimez, M., Pétillot, Y., Fallon, M. F., & Cremers, D. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460681 · Cited by 220 · see citing works
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Wang, H., Chen, W., & Xie, L. (2020) · IEEE Conference Proceedings · Cited by 222 · see citing works
FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow
Zhang, T., Zhang, H., Li, Y., Nakamura, Y., & Zhang, L. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197349 · Cited by 220 · see citing works
SLAM; definition and evolution
Taheri, H., & Xia, Z.-C. (2021) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2020.104032 · Cited by 187 · see citing works
ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera MotionPreprint
Zhang, J., Zhu, C., Zheng, L., & Xu, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05600 · Cited by 217 · see citing works
Unsupervised Scale-Consistent Depth Learning from Video
Bian, J.-W., Zhan, H., Wang, N., Li, Z., Zhang, L., Shen, C., Cheng, M., & Reid, I. D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01484-6 · Cited by 213 · see citing works
Blitz-SLAM: A semantic SLAM in dynamic environments
Fan, Y., Zhang, Q., Tang, Y., Liu, S., & Han, H. W. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108225 · Cited by 213 · see citing works
An Overview on Visual SLAM: From Tradition to Semantic
Chen, W.-F., Shang, G., Ji, A.-H., Zhou, C., Wang, X., Xu, C., Li, Z., & Hu, K. (2022) · Remote Sensing · doi:10.3390/rs14133010 · Cited by 217 · see citing works
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Pillai, S., Ambruş, R., & Gaidon, A. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793621 · Cited by 208 · see citing works
Data-Efficient Decentralized Visual SLAM
Cieslewski, T., Choudhary, S., & Scaramuzza, D. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461155 · Cited by 204 · see citing works
Zhang, J., Qian, S., & Tan, C. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105225 · Cited by 207 · see citing works
Visual Odometry Revisited: What Should Be Learnt?
Zhan, H., Weerasekera, C. S., Bian, J.-W., & Reid, I. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197374 · Cited by 201 · see citing works
Motion removal for reliable RGB-D SLAM in dynamic environments
Sun, Y., Liu, M., & Meng, M. Q. ‐. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.07.002 · Cited by 200 · see citing works
FAST-LIVO2: Fast, Direct LiDAR–Inertial–Visual Odometry
Zheng, C., Xu, W. D., Zou, Z., Hua, T., Yuan, C., He, D., Zhou, B., Liu, Z., Lin, J., Zhu, F., et al. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3502198 · Cited by 203 · see citing works
Robotic weed control using automated weed and crop classification
Wu, X., Aravecchia, S., Lottes, P., Stachniss, C., & Pradalier, C. (2020) · Journal of Field Robotics · doi:10.1002/rob.21938 · Cited by 196 · see citing works
Where Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning
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S-PTAM: Stereo Parallel Tracking and Mapping
Pire, T., Fischer, T., Castro, G. I., Cristóforis, P., Civera, J., & Jacobo-Berlles, J. (2017) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.03.019 · Cited by 187 · see citing works
Ravi Raj, K. G., & Kos, A. (2022) · Applied Sciences · doi:10.3390/app12146951 · Cited by 202 · see citing works
Georgiou, T., Liu, Y., Chen, W., & Lew, M. S. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00183-w · Cited by 194 · see citing works
TransVPR: Transformer-Based Place Recognition with Multi-Level Attention Aggregation
Wang, R., Shen, Y., Zuo, W., Zhou, S., & Zheng, N. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01328 · Cited by 190 · see citing works
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
Tang, J., Ericson, L., Folkesson, J. B., & Jensfelt, P. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2927954 · Cited by 188 · see citing works
Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning
Cimurs, R., Suh, I. H., & Lee, J. H. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3133591 · Cited by 187 · see citing works
A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision
Morar, A., Moldoveanu, A. D. B., Mocanu, I. G., Moldoveanu, F., Rǎdoi, E., Asavei, V., Grădinaru, A., & Butean, A. (2020) · Sensors · doi:10.3390/s20092641 · Cited by 189 · see citing works
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
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Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet
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Recasens, D., Lamarca, J., Fácil, J. M., Montiel, J. M. M., & Civera, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3095528 · Cited by 184 · see citing works
Ekanayake, B., Wong, J. K. W., Fini, A. A. F., & Smith, P. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103705 · Cited by 175 · see citing works
- Authors
- Mur-Artal, R., & Tardos, J. D.
- Affiliations
- Instituto de Investigación en Ingeniería de Aragón (ES)
- Venue
- IEEE Transactions on Robotics
- Published
- 2017-10
- Volume
- 33
- Issue
- 5
- Pages
- 1255-1262
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2535547924
- Cited by
- 6,898
- References
- 21
- Citations by year
- 2026: 521 · 2025: 851 · 2024: 944 · 2023: 962 · 2022: 910 · 2021: 870 · 2020: 754 · 2019: 636 · 2018: 360 · 2017: 90
- FWCI
- 463.87
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Spanish government (DPI2015-67275)
- Aragón regional government (DGA T04-FSE)
- Ministerio de Educación Scholarship (FPU13/04175)
- 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 › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- ORB-SLAM2, SLAM, monocular SLAM, stereo SLAM, RGB-D SLAM, loop closure, relocalization, bundle adjustment, visual odometry, trajectory estimation
About these checks
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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