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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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“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]
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“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]
Experimental — accuracy not yet measured
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Citations
Cites
ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037 · cited 1×
“The system uses the same ORB features [17] for tracking, mapping, and place recognition tasks.”
MethodVision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · cited 3×
“We use two different metrics, the absolute translation root-mean-square error (RMSE) tabs proposed in [3], and the average relative translation trel and rotation rrel errors proposed in [2].”
BackgroundMethodORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778 · cited 7×
“Left: Monocular ORB-SLAM [1], right: ORB-SLAM2 (stereo).”
BackgroundMethodResultInfluentialSelf-citationA 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 · cited 4×
“The RGB-D case shows the keyframe poses estimated in sequence fr1_room from the TUM RGB-D Dataset [3], and a dense pointcloud, rendered by backprojecting sensor depth maps from the estimated keyframe poses.”
MethodInfluentialKinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008 · cited 2×
“Note that our SLAM does not perform any fusion like KinectFusion [4] or similar, but the good definition indicates the accuracy of the keyframe poses.”
BackgroundThe 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 · cited 1×
“The recent EuRoC dataset [21] contains 11 stereo sequences recorded from a microaerial vehicle (MAV) flying around two different rooms and a large industrial environment.”
BackgroundBags of Binary Words for Fast Place Recognition in Image Sequences
Gálvez‐López, D., & Tardós, J. D. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2197158 · Cited by 1,890 · cited 2×
“The system has embedded a Place Recognition module based on DBoW2 [16] for relocalization, in case of tracking failure (e.g. an occlusion) or for reinitialization in an already mapped scene, and for loop detection.”
Dense visual SLAM for RGB-D cameras
Kerl, C., Sturm, J., & Cremers, D. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696650 · Cited by 941 · cited 5×
“In Table III we compare our accuracy to the following state-of-the-art methods: ElasticFusion [15], Kintinuous [12], DVO-SLAM [14] and RGB-D SLAM [13].”
MethodInfluential3-D Mapping With an RGB-D Camera
Endres, F., Hess, J., Sturm, J., Cremers, D., & Burgard, W. (2014) · IEEE Transactions on robotics · doi:10.1109/tro.2013.2279412 · Cited by 821 · cited 5×
“In Table III we compare our accuracy to the following state-of-the-art methods: ElasticFusion [15], Kintinuous [12], DVO-SLAM [14] and RGB-D SLAM [13].”
MethodInfluentialVisual-Inertial Monocular SLAM With Map Reuse
Mur-Artal, R., & Tardós, J. D. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2653359 · Cited by 790 · cited 1×
“As shown in [22], this sequence can be processed using IMU information.”
MethodSelf-citationInverse Depth Parametrization for Monocular SLAM
Civera, J., Davison, A. J., & Montiel, J. M. M. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2003276 · Cited by 768 · cited 1×
“, points whose depth cannot be reliably estimated due to little disparity in the stereo camera), using an inverse depth parameterization [6] for the latter.”
MethodElasticFusion: Real-time dense SLAM and light source estimation
Whelan, T. J., Salas-Moreno, R. F., Glocker, B., Davison, A. J., & Leutenegger, S. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364916669237 · Cited by 694 · cited 4×
“[15] builds a surfel-based map of the environment.”
BackgroundMethodInfluentialLarge-scale direct SLAM with stereo cameras
Engel, J. J., Stückler, J., & Cremers, D. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353631 · Cited by 566 · cited 7×
“The recent Stereo LSD-SLAM of Engel et al. [11] is a semi-dense direct approach that minimizes photometric error in image regions with high gradient.”
BackgroundResultInfluentialStrasdat, H., Montiel, J. M. M., & Davison, A. J. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.009 · Cited by 439 · cited 1×
“Most modern stereo SLAM systems are keyframe based [7] and perform BA optimization in a local area to achieve scalability.”
BackgroundScale Drift-Aware Large Scale Monocular SLAM
Strasdat, H., Montiel, J. M. M., & Davison, A. (2010) · Robotics: Science and Systems · doi:10.15607/rss.2010.vi.010 · Cited by 396 · cited 1×
“In contrast to monocular ORB-SLAM, where scale drift may occur [20], the stereo/depth information makes scale observable and the geometric validation and pose-graph optimization no longer require dealing with scale drift and are based on rigid body transformations instead of similarities.”
BackgroundDouble window optimisation for constant time visual SLAM
Strasdat, H., Davison, A. J., Montiel, J. M. M., & Konolige, K. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126517 · Cited by 296 · cited 4×
“[8] performs a joint optimization of BA (point-pose constraints) in an inner window of keyframes and pose graph (pose-pose constraints) in an outer window.”
BackgroundMethodInfluentialFast relocalisation and loop closing in keyframe-based SLAM
Mur-Artal, R., & Tardós, J. D. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6906953 · Cited by 262 · cited 1×
“Moreover, they are fast to extract and match allowing for real-time operation and show good precision/recall performance in bag-of-word place recognition [18].”
BackgroundSelf-citationLarge-Scale 6-DOF SLAM With Stereo-in-Hand
Paz, L. M., Piniés, P., Tardós, J. D., & Neira, J. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004637 · Cited by 241 · cited 1×
“A stereo keypoint is classified as close if its associated depth is less than 40 times the stereo/RGB-D baseline, as suggested in [5], otherwise it is classified as far.”
BackgroundRSLAM: A System for Large-Scale Mapping in Constant-Time Using Stereo
Christopher Mei, A., Sibley, G., Cummins, M., Newman, P. M., & Reid, I. D. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0361-7 · Cited by 192 · cited 1×
“[9] uses a relative representation of landmarks and poses and performs relative BA in an active area, which can be constrained for constant time.”
BackgroundStereo parallel tracking and mapping for robot localization
Pire, T., Fischer, T., Civera, J., Cristoforis, P. D., & Berlles, J. J. (2015) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros.2015.7353546 · Cited by 120 · cited 1×
“[10] performs local BA, however it lacks large loop closing.”
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1 of the 21 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- 10.1177/0278364914551008 · DOI
- kuemmerle, 0, g2o: A general framework for graph optimization, Proc IEEE Int Conf Robot Autom, 3607
Cited by
Sorted by citations
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM
Campos, C., Elvira, R., Rodríguez, J. J. G., Montiel, J. M. M., & Tardos, J. D. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3075644 · Cited by 4,676
Self-citationDynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
Bescos, B., Fácil, J. M., Civera, J., & Neira, J. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2860039 · Cited by 1,333
“All the methods –both feature-based and direct ones– that map the static scene parts only from the information contained in the sequence [1], [3], [9], [12]–[17], fail to estimate lifelong models when an a priori dynamic object remains static, e.”
BackgroundMethodInfluentialHabitat: A Platform for Embodied AI Research
Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00943 · Cited by 1,237
“We use the classic agent by Mishkin et al. [19] which leverages the ORB-SLAM2 [20] localization pipeline, with the same parameters as reported in the original work.”
MethodDS-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
“DS-SLAM adopt ORB-SLAM2 [1] as global SLAM solution, which it is recognized as one of the most outstanding and stable SLAM algorithms at present, so we make a comparison between them.”
BackgroundLabbé, M., & Michaud, F. (2018) · J. Field Robotics · doi:10.1002/rob.21831 · Cited by 1,009
Metadata
- 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 › 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 › 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
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