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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]
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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-citation
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Globally Consistent 3D LiDAR Mapping With GPU-Accelerated GICP Matching Cost Factors
Koide, K., Yokozuka, M., Oishi, S., & Banno, A. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3113043 · Cited by 57
“Bundle adjustment (BA) that simultaneously optimizes sensor poses and environmental parameters over frames has been important in the visual SLAM field [20].”
BackgroundAn Object SLAM Framework for Association, Mapping, and High-Level Tasks
Wu, Y., Zhang, Y., Zhu, D., Deng, Z., Sun, W., Chen, X., & Zhang, J. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3273180 · Cited by 52
“[52] R. Mur-Artal and J. D. Tardós, “ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras,” IEEE Trans.”
BackgroundMethodInfluentialA novel measurement model based on abBRIEF for global localization of a UAV over satellite images
Mantelli, M., Pittol, D., Neuland, R., Ribacki, A., Maffei, R. D. Q., Jorge, V. A. M., Prestes, E., & Kolberg, M. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.12.006 · Cited by 52
Towards Robust Visual Odometry with a Multi-Camera System
Liu, P., Geppert, M., Heng, L., Sattler, T., Geiger, A., & Pollefeys, M. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593561 · Cited by 51
Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion
Romero-Ramírez, F. J., Muñoz‐Salinas, R., & Medina-Carnicer, R. (2019) · IEEE Access · doi:10.1109/access.2019.2951204 · Cited by 52
“Solutions using natural features have attracted most of the research effort, reaching a high degree of performance [1], [2].”
BackgroundMethodMLDA-Net: Multi-Level Dual Attention-Based Network for Self-Supervised Monocular Depth Estimation
Song, X., Li, W., Zhou, D., Dai, Y., Fang, J., Li, H., & Zhang, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3074306 · Cited by 53
“image-based scene understanding [1]–[4], SLAM [5]–[7], augmented reality [8], [9] and autonomous driving [10]–[12].”
BackgroundSajad Saeedi, G., Spink, T., Gorgovan, C., Webb, A. M., Clarkson, J., Tomusk, E., Debrunner, T., Kaszyk, K., Marugán, P. G. D. A., Rodchenko, A., et al. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2856739 · Cited by 51
“…Dense CUDA InfiniTAM [34] Dense C++, OpenMP, CUDA KinectFusion [7] Dense C++, OpenMP, OpenCL, CUDA LSD-SLAM [35] Semi-Dense C++, PThread ORB-SLAM2 [36] Sparse C++ MonoSLAM [37] Sparse C++, OpenCL OKVIS [38] Sparse C++ PTAM [5] Sparse C++ SVO [39] Sparse C++ Third parties have provided…”
Multi-user augmented reality with communication efficient and spatially consistent virtual objects
Ran, X., Slocum, C., Tsai, Y.-Z., Apicharttrisorn, K., Gorlatova, M. A., & Chen, J. (2020) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3386367.3431312 · Cited by 51
“Other open-source SLAM systems are either not tested on Android [16, 46] or do not utilize IMU sensors [38].”
BackgroundMethodReal-Time HD Map Change Detection for Crowdsourcing Update Based on Mid-to-High-End Sensors
Zhang, P., Zhang, M., & Liu, J. (2021) · Sensors · doi:10.3390/s21072477 · Cited by 51
“Mur-Artal, R.; Tardos, J.D. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras.”
MethodModest-vocabulary loop-closure detection with incremental bag of tracked words
Tsintotas, K. A., Bampis, L., & Gasteratos, A. C. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103782 · Cited by 51
Robust Visual-Lidar Simultaneous Localization and Mapping System for UAV
Qian, J., Chen, K., Chen, Q., Yang, Y., Zhang, J., & Chen, S. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3099166 · Cited by 51
“ORB-SLAM2 [18] is a classic open source SLAM algorithm based on feature points matching, which finds matching feature points through descriptors and uses reprojection errors for pose estimation.”
MethodCamera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey
Chghaf, M., Rodriguez, S., & Ouardi, A. E. (2022) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-022-01582-8 · Cited by 53
“The implementation uses LOAM pipeline to generate a voxel map and an ORB-SLAM2 pipeline to obtain a visual map.”
BackgroundMethodInfluentialFang, X., Li, Q., Zhu, J., Chen, Z., Zhang, D., Wu, K., Ding, K., & Li, Q.-Q. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104494 · Cited by 42
Improving RGB-D SLAM accuracy in dynamic environments based on semantic and geometric constraints
Wang, X., Zheng, S., Lin, X., & Zhu, F. (2023) · Measurement · doi:10.1016/j.measurement.2023.113084 · Cited by 52
RelPose++: Recovering 6D Poses from Sparse-view Observations
Lin, A., Zhang, J. Y., Ramanan, D., & Tulsiani, S. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00126 · Cited by 52
“This paradigm has been scaled by efficient parallelization [40, 41] and can even run in real-time for visual odometry [3,29,30].”
MethodSuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
Dong, H., Gu, W., Zhang, X., Xu, J., Ai, R., Lu, H., Kannala, J., & Chen, X. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611320 · Cited by 51
“The traditional way of reconstructing HD semantic maps is to aggregate LiDAR point clouds using SLAM algorithms [26] and then annotate manually, which is laborious and difficult to update.”
MethodLipson, L., Teed, Z., & Deng, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72627-9_24 · Cited by 52
LoopSplat: Loop Closure by Registering 3D Gaussian Splats
Zhu, L., Li, Y., Sandström, E., Huang, S., Konrad Schindler, B., & Armeni, I. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00020 · Cited by 77
“…via loop closure and/or global bundle adjustment (BA), GO-SLAM [101] requires costly retraining of the hash grid features to de-1 form the map and Photo-SLAM [30] similarly requires additional optimization of the 3D Gaussian parameters to resolve pose updates from the ORB-SLAM [52] tracker.”
Test Your SLAM! The SubT-Tunnel dataset and metric for mapping
Rogers, J. G., Gregory, J. M., Fink, J., & Stump, E. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197156 · Cited by 51
“ORB SLAM2 [6] is a robot mapping system that consumes camera data instead of LiDAR, either monocular, stereo, or depth camera (RGB-D).”
BackgroundRedesigning SLAM for Arbitrary Multi-Camera Systems
Kuo, J., Muglikar, M., Zhang, Z., & Scaramuzza, D. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-193888 · Cited by 51
LiDAR-Based 3D SLAM for Indoor Mapping
Chan, T. H., Hesse, H., & Ho, S. G. (2021) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar52225.2021.9463503 · Cited by 50
“Current visual-based SLAM frameworks such as [5] and [6] rely on monocular or RGB-D cameras for real time mapping of the environment.”
BackgroundKuo, C.-Y., Huang, C.-C., Tsai, C.-H., Shi, Y.-S., & Smith, S. S.-F. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103502 · Cited by 50
Unsupervised Monocular Depth Estimation in Highly Complex Environments
Zhao, C., Tang, Y. S., & Sun, Q. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3182360 · Cited by 55
“methods are widely used in traditional SLAM methods to map the environments [5], [6].”
MethodA survey of visual navigation: From geometry to embodied AI
Zhang, T., Hu, X., Xiao, J., & Zhang, G. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105036 · Cited by 53
Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection
Yang, M., Chen, Y., & Kim, H.-S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_14 · Cited by 56
A systematic literature review on long‐term localization and mapping for mobile robots
Sousa, R. B., Sobreira, H., & Moreira, A. P. (2023) · Journal of Field Robotics · doi:10.1002/rob.22170 · Cited by 56
Ye, W., Lan, X., Chen, S., Ming, Y., Yu, X., Bao, H., Cui, Z., & Zhang, G. (2023) · 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/cvpr52729.2023.00924 · Cited by 51
“RMSE of trajectory alignment [m] DVO SLAM [16] ORB-SLAM2 [28] PointCorr [6] DROID-SLAM [34] Ours”
MethodTightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
Gakne, P. V., & O’Keefe, K. (2018) · Sensors · doi:10.3390/s18041244 · Cited by 49
“In [19], simultaneous localization and mapping (SLAM) methods are proposed using different camera configurations (monocular versus stereo), as well as different types of cameras (monocular/stereo visual spectral cameras versus RGB-D cameras).”
MethodZhang, Z., Zhao, R., Liu, E., Yan, K., & Ma, Y. (2018) · Sensors · doi:10.3390/s18061948 · Cited by 50
Large-Scale Outdoor SLAM Based on 2D Lidar
Ren, R., Fu, H., & Wu, M.-P. (2019) · Electronics · doi:10.3390/electronics8060613 · Cited by 40
Visual-Odometric Localization and Mapping for Ground Vehicles Using SE(2)-XYZ Constraints
Fan, Z., & Liu, Y. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793928 · Cited by 50
“We compare our work (termed SE2 here) with 1) the SE(2)-constrained system (termed SE2C) in [19] and 2) an odometric-aided version of the state-of-the-art visual estimation system ORB-SLAM2 [5] on the two datasets.”
MethodSemantic SLAM with Autonomous Object-Level Data Association
Qian, Z., Patath, K., Fu, J., & Xiao, J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561532 · Cited by 50
“The utilized SLAM tracking module is inherited from ORB-SLAM2 [10].”
InfluentialA Study of 3D Digitisation Modalities for Crime Scene Investigation
Galanakis, G., Zabulis, X., Evdaimon, T., Fikenscher, S.-E., Allertseder, S., Tsikrika, T., & Vrochidis, S. (2021) · Forensic Sciences · doi:10.3390/forensicsci1020008 · Cited by 49
“In the context of our investigation with digitisation tools, we developed an RGB-D scanning modality based on the state-of-the-art RGB-D SLAM and reconstruction [54] tailored for this type of sensor.”
Sensing and Navigation of Wearable Assistance Cognitive Systems for the Visually Impaired
Li, G., Xu, J., Li, Z., Chen, C., & Kan, Z. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3146828 · Cited by 49
“1) The indoor navigation subsystem combines the ORB-SLAM2 algorithm [35] and the enhanced navigation method to perform indoor positioning and navigation.”
MethodChi, G., Yang, Z., Xu, J., Wu, C., Zhang, J., Liang, J., & Liu, Y. (2022) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3498361.3538936 · Cited by 52
“Based on the kinetic characteristics of the drone, the constant velocity model, which has been widely used in both flight control [40] and poses estimation [25], is adopted to derive the prior term.”
MethodFeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network
Wang, X., Liu, Z., Hu, Y., Xi, W., Yu, W., & Zou, D. (2023) · 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/cvpr52729.2023.00737 · Cited by 49
A Cloud and Vision-based Navigation System Used for Blind People
Bai, J., Liu, D., Su, G., & Fu, Z. (2017) · International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT) · doi:10.1145/3080845.3080867 · Cited by 53
Employing Natural Terrain Semantics in Motion Planning for a Multi-Legged Robot
Belter, D., Wietrzykowski, J., & Skrzypczyński, P. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-018-0865-x · Cited by 49
Real-Time Global Registration for Globally Consistent RGB-D SLAM
Han, L., Xu, L., Bobkov, D., Steinbach, E. G., & Fang, L. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2882730 · Cited by 47
“In general, as indicated in [1], [10], and [13], although PGO is much more efficient for realtime applications such as robot/UAV exploration [14], most state-of-the-art SLAM systems aiming for high-quality three-dimensional (3-D) reconstruction and accurate pose estimations [8]–[10] still prefer global registration fo…”
BackgroundMethodInfluentialLidar Mapping Optimization Based on Lightweight Semantic Segmentation
Zhao, Z., Zhang, W., Gu, J., Yang, J., & Huang, K. (2019) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2019.2919432 · Cited by 49
“[22] R. Mur-Artal and J. D. Tardós, “ORB-SLAM2: An open-source slam system for monocular, stereo, and RGB-D cameras,” IEEE Trans.”
BackgroundMademlis, I., Nikolaidis, N., Tefas, A., Pitas, I., Wagner, T., & Messina, A. (2019) · ACM Computing Surveys · doi:10.1145/3347713 · Cited by 49
“Multiple-UAV coordination in a swarm context also allows cooperative variants of algorithms from the vision and the 3D-group to be employed, e.g., cooperative visual SLAM [58] or cooperative path planning [81].”
MethodInfluentialSurvey on path and view planning for UAVs
Zhou, X., Yi, Z., Liu, Y., Huang, K., & Huang, H. F. (2020) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2019.12.004 · Cited by 49
“Mur-Artal et al. [28] and Mur-Artal et al. [25] utilized features detected between images to construct geometric information for target scenes, localize a robot, and perform optimization to fine-tune parameters.”
BackgroundResearch on SLAM Algorithm of Mobile Robot Based on the Fusion of 2D LiDAR and Depth Camera
Mu, L., Yao, P., Zheng, Y., Chen, K., Wang, F., & Qi, N. (2020) · IEEE Access · doi:10.1109/access.2020.3019659 · Cited by 51
“VSLAM then entered a period of rapid development and a large number of excellent algorithmic frameworks emerged, ORB-SLAM [22], [23], DTAM [24], LSD-SLAM [25]–[27], SVO [28] and DVO-SLAM [29].”
BackgroundReal-Time Scene-Aware LiDAR Point Cloud Compression Using Semantic Prior Representation
Zhao, L., Ma, K., Liu, Z., Yin, Q., & Chen, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3145513 · Cited by 48
RGBDTAM: A cost-effective and accurate RGB-D tracking and mapping system
Concha, A., & Civera, J. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206593 · Cited by 49
“We have used the same sequences than the original paper [27].”
BackgroundMethodInfluentialA Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving
Liu, H., Ye, Q., Wang, H., Chen, L., & Yang, J. J. (2019) · Remote Sensing · doi:10.3390/rs11111348 · Cited by 48
“Raul et al. present ORB-SLAM2 [21,22], a complete feature-based visual SLAM system for monocular, stereo, and RGB-D (Color-Depth) cameras, including map reuse, DBoW2-based [23] loop closing, and relocalization capabilities.”
Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry
Andraghetti, L., Myriokefalitakis, P., Dovesi, P. L., Luque, B., Poggi, M., Pieropan, A., & Mattoccia, S. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00054 · Cited by 48
“Large progress has been achieved in the development of VO and SLAM methods [9, 10, 31, 32].”
BackgroundMethodAn Online Initialization and Self-Calibration Method for Stereo Visual-Inertial Odometry
Huang, W., Liu, H., & Wan, W. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2959161 · Cited by 48
“The tracking and local mapping threads of the ORBSLAM [15], [16] system is adopted to implement the KeyFrame Generation module.”
MethodVOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals
Min, Z., Yang, Y., & Dunn, E. (2020) · 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/cvpr42600.2020.00495 · Cited by 47
“Traditional indirect VO [47, 64, 57] methods rely on the geometric analysis of sparse keypoint correspondences to determine multi-view relationships among the input video frames.”
BackgroundMethodReal-Time UAV Path Planning for Autonomous Urban Scene Reconstruction
Kuang, Q., Wu, J., Pan, J., & Zhou, B. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196558 · Cited by 50
“[12] proposed a SLAM system using orb features for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities.”
MethodRobust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion
Wu, Y., Li, Y., Li, W., Li, H., & Lu, R. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027983 · Cited by 49
A Template-Based 3D Reconstruction of Colon Structures and Textures From Stereo Colonoscopic Images
Zhang, S., Zhao, L., Huang, S.-D., Ye, M., & Hao, Q. (2021) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2020.3044108 · Cited by 45
“Furthermore, the poor texture of colonoscopic images is a challenge for feature-point-based SLAM methods, e.g., ORB-SLAM2 [15].”
BackgroundMethodEdge-SLAM: edge-assisted visual simultaneous localization and mapping
Ben Ali, A. J., Hashemifar, Z. S., & Dantu, K. (2020) · Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3386901.3389033 · Cited by 46
Automated Object Manipulation Using Vision-Based Mobile Robotic System for Construction Applications
Asadi, K., Haritsa, V. R., Han, K. K., & Ore, J. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000946 · Cited by 52
Semantic SLAM Based on Improved DeepLabv3⁺ in Dynamic Scenarios
Hu, Z., Zhao, J., Luo, Y., & Ou, J. (2022) · IEEE Access · doi:10.1109/access.2022.3154086 · Cited by 47
“At present, most visual SLAM frameworks operate under the assumption of a static environment, such as ORB-SLAM [1], ORB-SLAM2 [2], ORB-SLAM3 [3], LSD-SLAM [4], RGB-D SLAM [5].”
BackgroundA Real-Time Stereo Visual-Inertial SLAM System Based on Point-and-Line Features
Liu, X., Wen, S.-H., & Zhang, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3233721 · Cited by 44
“Some classical visual SLAM, such as ORB-SLAM2 [9], LSD-SLAM [15], and DSO [16], exhibit good performance.”
BackgroundDeng, T., Xie, H., Wang, J., & Chen, W. (2023) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2022.3228492 · Cited by 47
“Our method is more accurate compared to the methods that use the static-world assumption [14], [15].”
MethodResultInfluentialPLDS-SLAM: Point and Line Features SLAM in Dynamic Environment
Yuan, C., Xu, Y., & Zhou, Q. (2023) · Remote Sensing · doi:10.3390/rs15071893 · Cited by 48
AGRI-SLAM: a real-time stereo visual SLAM for agricultural environment
Islam, R., Habibullah, H., & Hossain, T. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10110-y · Cited by 49
“VisualSLAMmethodsaremainlysubdividedintotwocat-egories: Feature-based method (Mur-Artal & Tardós, 2017; Quan et al., 2021) and direct method (Engel et al., 2014).”
BackgroundMethodResultInfluentialZhong, M., Hong, C., Jia, Z., Wang, C., & Wang, Z. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2024.104634 · Cited by 49
RSO-SLAM: A Robust Semantic Visual SLAM With Optical Flow in Complex Dynamic Environments
Qin, L., Wu, C. W., Chen, Z., Kong, X., Lv, Z., & Zhao, Z. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3402241 · Cited by 48
“Consequently, several high-performance VSLAM systems have been developed, including DSO [1], LSD-SLAM [2], and ORB-SLAM2 [3].”
MethodInfluentialDirect Sparse Odometry with Rolling Shutter
Schubert, D., Demmel, N., Usenko, V., Stückler, J., & Cremers, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_42 · Cited by 47
Redesigning SLAM for Arbitrary Multi-Camera Systems
Kuo, J., Muglikar, M., Zhang, Z., & Scaramuzza, D. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197553 · Cited by 57
“In contrast, stereo cameras can recover the depth information and initialize the map directly [9], [10].”
BackgroundTightly-Coupled Single-Anchor Ultra-wideband-Aided Monocular Visual Odometry System
Nguyen, T. H., Nguyen, T., & Xie, L. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196794 · Cited by 46
“A new thread for estimating the scale factor and the anchor position as well as monitoring ranging errors is augmented on top of the existing ORB-SLAM [6] framework.”
MethodA Deep Reinforcement Learning Approach for Active SLAM
Placed, J. A., & Castellanos, J. A. (2020) · Applied Sciences · doi:10.3390/app10238386 · Cited by 50
“As future work, we aim to embed a more complex state-of-the-art SLAM system to obtain a more precise uncertainty estimate which also includes a state representation based on Lie algebras, e.g., ORB-SLAM2 [39], in order to accurately compute optimality criteria, see [9].”
MethodMultirobot Collaborative Monocular SLAM Utilizing Rendezvous
Jang, Y., Oh, C., Lee, Y.-W., & Kim, H. J. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3058502 · Cited by 47
MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry
Joan P. Company-Corcoles, Garcia‐Fidalgo, E., & Ortiz, A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3142900 · Cited by 47
“Many VO and SLAM systems rely on point features because of their wider applicability in general terms [2].”
BackgroundMethodInfluentialZhou, H., & Jayender, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_32 · Cited by 46
Fast Direct Stereo Visual SLAM
Mo, J., Islam, M. J., & Sattar, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3133860 · Cited by 48
Visual SLAM algorithms and their application for AR, mapping, localization and wayfinding
Theodorou, C., Velisavljević, V., Dyo, V., & Nonyelu, F. (2022) · Array · doi:10.1016/j.array.2022.100222 · Cited by 55
Yang, L., & Cai, H. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102313 · Cited by 48
Sucar, E., & Hayet, J.-B. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461178 · Cited by 45
Atapour–Abarghouei, A., & Breckon, T. P. (2018) · Computers & Graphics · doi:10.1016/j.cag.2018.02.001 · Cited by 46
“However, other applications such as localization and mapping [78, 79] do not require fine texture and relief for each individ-”
BackgroundSLAM of Robot based on the Fusion of Vision and LIDAR
Xu, Y., Ou, Y., & Xu, T. (2018) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs.2018.8612212 · Cited by 45
RESLAM: A real-time robust edge-based SLAM system
Schenk, F., & Fraundorfer, F. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794462 · Cited by 47
“The current state-of-the-art featurebased system ORB-SLAM2 [4] utilizes ORB features for tracking, mapping and loop closing and also supports stereo and monocular setups.”
BackgroundMethodInfluentialROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems
Seok, H., & Lim, J. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793758 · Cited by 47
“For robotic application, stereo-based systems [1], [2] have been proposed.”
Tightly Coupled Integration of GNSS and Vision SLAM Using 10-DoF Optimization on Manifold
Gong, Z., Ying, R., Wen, F., Qian, J., & Liu, P. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2935387 · Cited by 44
“The system is modified based on the ORB-SLAM2 system [20].”
MethodInfluentialA Friction-Based Kinematic Model for Skid-Steer Wheeled Mobile Robots
Rabiee, S., & Biswas, J. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794216 · Cited by 46
“The logged data is then used for generating the ground truth trajectory traversed by the robot offline and using ORB-SLAM [12].”
MethodFunabiki, N., Morrell, B. J., Nash, J., & Agha–mohammadi, A. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3026659 · Cited by 46
“Semantic techniques generate a description of the environment to robustly match similar locations, and include algorithms such as Bag of Words [2], [12], [13] or NetVLAD [14] for visual approaches, and SegMatch [15], or PointNetVLAD [16] for lidar-based approaches.”
MethodHigh-Precision Multicamera-Assisted Camera-IMU Calibration: Theory and Method
Fu, B., Han, F., Wang, Y., Jiao, Y., Ding, X., Tan, Q., Chen, L., Wang, M., & Xiong, R. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3051726 · Cited by 47
“Following the definition of the image feature point error by the previous camera-IMU state estimation algorithm Orb-slam2 [18] and maplab [36], it is supposed that the random noise obeys the Gauss distribution N ( 0 , σ 2 ) .”
MethodShu, F., Lesur, P., Xie, Y., Pagani, A., & Stricker, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00180 · Cited by 50
“erformance and robustness in agricultural settings difficult, as we highlight later in this work. This led us to work with the recently released framework OpenVSLAM [59] which was built upon ORB-SLAM2 [40] without specific change on the core algorithms but provides a new framework with high usability and extensibility.…”
MethodInfluentialAn Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation
Qu, Y., Yang, M., Zhang, J., Xie, W., Qiang, B., & Chen, J. (2021) · Sensors · doi:10.3390/s21051605 · Cited by 48
“At present, there are a few mature RGB-D visual navigation solutions, such as RGB-D SLAM V2 [27], dense visual odometry-SLAM (DVO-SLAM) [28], and RTAB-MAP [22], etc.”
BackgroundMethodInfluentialMultimodal sensor-based semantic 3D mapping for a large-scale environment
Jeong, J., Yoon, T. S., & Park, J. B. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.051 · Cited by 47
HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry
Seiskari, O., Rantalankila, P., Kannala, J., Ylilammi, J., Rahtu, E., & Solin, A. (2021) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00036 · Cited by 33
“We used OpenVSLAM [36], a re-implementation of the ORBSLAM2 [20] method, as the basis for the implementation.”
Method360VO: Visual Odometry Using A Single 360 Camera
Huang, H., & Yeung, S.-K. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812203 · Cited by 43
“Various systems (e.g., PTAM [10], SVO [1], LSD-SLAM [3], DSO [4], ORB-SLAM2 [5], etc.) have been proposed and can deal with monocular, stereo and RGBD cameras, significantly stimulating the development of our community.”
BackgroundMethodA Review of Recurrent Neural Network Based Camera Localization for Indoor Environments
Alam, M. S., Mohamed, F., Selamat, A., & Hossain, A. B. (2023) · IEEE Access · doi:10.1109/access.2023.3272479 · Cited by 46
“ORB-SLAM2 [34] was developed in 2016 in response to ORB drawbacks.”
BackgroundMethodInfluentialAdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization
Chen, Y., İnaltekin, H., & Gorlatova, M. A. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229009 · Cited by 44
“It can be classified into direct approaches (LSD-SLAM [22], DSO [23]), which operate directly on pixel intensity values, and featurebased approaches (PTAM [24], ORB-SLAM2 [18], PairNavi [25]), which extract salient regions in each camera frame.”
BackgroundA Survey of Visual SLAM Methods
Sahili, A. R., Hassan, S. A., Sakhrieh, S., Mounsef, J., Maalouf, N., Arain, B., & Taha, T. (2023) · IEEE Access · doi:10.1109/access.2023.3341489 · Cited by 52
“The algorithm consists of three main components: tracking, mapping, and loop closure detection [12], [34], [47].”
BackgroundMethodInfluentialA survey of feature matching methods
Huang, Q., Guo, X., Wang, Y., Sun, H., & Yang, L. (2024) · IET Image Processing · doi:10.1049/ipr2.13032 · Cited by 45
Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
Heshmat, M., Saoud, L. S., Abujabal, M., Sultan, A., Elmezain, M., Seneviratne, L. D., & Hussain, I. (2025) · Italian National Conference on Sensors · doi:10.3390/s25113258 · Cited by 48
Nguyen, D. D., Elouardi, A., Rodríguez, S., & Bouaziz, S. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2881556 · Cited by 44
“contributes with a stereo version of ORB-SLAM in [1] to handle the problem of scale drift.”
BackgroundSDF-SLAM: Semantic Depth Filter SLAM for Dynamic Environments
Cui, L., & Ma, C. (2020) · IEEE Access · doi:10.1109/access.2020.2994348 · Cited by 46
“Many excellent visual SLAM systems have been developed, such as MonoSLAM [1], PTAM [2], ORB-SLAM [3], ORB-SLAM2 [4], LSD-SLAM [5], SVO [6], DSO [7].”
MethodMinimal Solutions for Relative Pose With a Single Affine Correspondence
Guan, B., Zhao, J. Y., Li, Z., Sun, F., & Fraundorfer, F. (2020) · 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/cvpr42600.2020.00200 · Cited by 45
Learning Steering Kernels for Guided Depth Completion
Liu, L., Liao, Y., Wang, Y., Geiger, A., & Liu, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3055629 · Cited by 45
“This is particularly valuable in applications where the sparse measurements are obtained from visual SLAM, in which case the number of points might differ at every frame.”
BackgroundInfluentialLiDAR-Based Glass Detection for Improved Occupancy Grid Mapping
Tibebu, H., Roche, J., Silva, D., & KONDOZ, A. M. (2021) · Sensors · doi:10.3390/s21072263 · Cited by 45
MC-Calib: A generic and robust calibration toolbox for multi-camera systems
Rameau, F., Park, J., Bailo, O., & Kweon, I. S. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103353 · Cited by 46
Esparza, D., & Flores, G. (2022) · IEEE Access · doi:10.1109/access.2022.3149885 · Cited by 45
“Linyan Cui and Chaowei Ma [13] proposed the SOF-SLAM, an RGB-D system based on ORB-SLAM2, which combines a neural network for semantic segmentation, and optical flow for removing dynamic features.”
MethodInfluentialA fast, complete, point cloud based loop closure for LiDAR odometry and mappingPreprint
Lin, J., & Zhang, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11811 · Cited by 45
“This kind of method has been used in most of the state of the art visual SLAM system (e.g. [8, 9]) and have achieved great success in the past years.”
MethodHafi, L. E., Isobe, S., Tabuchi, Y., Katsumata, Y., Nakamura, H., Fukui, T., Matsuo, T., Ricardez, G. A. G., Yamamoto, M., Taniguchi, A., et al. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1694068 · Cited by 44
Interactive defect quantification through extended reality
Al‐Sabbag, Z. A., Yeum, C. M., & Narasimhan, S. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101473 · Cited by 44
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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