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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
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
Items 401–500 of 1,004
Zhang, M., Zuo, X., Chen, Y., Liu, Y., & Li, M. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3043970 · Cited by 63
“The errors used for optimization can be either under geometrical form [28], [31] or photometric form [29], [30].”
BackgroundMethodThe MADMAX data set for visual‐inertial rover navigation on Mars
Meyer, L., Smíšek, M., Fontan, A., Maza, L. O., Medina, D., Schuster, M. J., Steidle, F., Vayugundla, M., Muller, M. G., Rebele, B., et al. (2021) · Journal of Field Robotics · doi:10.1002/rob.22016 · Cited by 62
Xiong, L., Kang, R., Zhao, J., Zhang, P., Xu, M., Ju, R., Ye, C., & Feng, T. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107873 · Cited by 62
Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes
Wen, S., Li, X. S., Liu, X., Li, J., Tao, S., Long, Y., & Qiu, T. Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3317378 · Cited by 62
“Our system is based on the ORB-SLAM2 framework [36], which is an excellent feature-based SLAM framework.”
MethodResultLoopy-SLAM: Dense Neural SLAM with Loop Closures
Liso, L., Sandström, E., Yugay, V., Gool, L. V., & Oswald, M. R. (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/cvpr52733.2024.01925 · Cited by 77
“Orbeez-SLAM [9] and NEWTON [29] use a decoupled approach by employing ORB-SLAM2 [32] as the tracking system.”
MethodOdometry-Vision-Based Ground Vehicle Motion Estimation With SE(2)-Constrained SE(3) Poses
Fan, Z., Tang, H., & Liu, Y. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2831900 · Cited by 61
“As seen from the plots, both ORB-SLAM2 and our method can reduce and limit the accumulated errors of the odometry, due to the graph optimization back-end and loop closure.”
BackgroundMethodInfluentialA Framework for UAV Navigation and Exploration in GPS-Denied Environments
Vanegas, F., Gaston, K. J., Roberts, J. M., & Gonzalez, F. (2019) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2019.8741612 · Cited by 65
“A second boolean variable ôb indicates when a landmark is detected by the UAV front camera and/or SLAM system and is part of the observation.”
MethodInfluentialCartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler
Dwijotomo, A., Rahman, M. A. A., Ariff, M. H. M., Zamzuri, H., & Azree, W. M. H. B. W. (2020) · Applied Sciences · doi:10.3390/app10010347 · Cited by 62
DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization
Hu, H., Qiao, Z., Cheng, M., Liu, Z., & Wang, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3043875 · Cited by 66
“Traditional local feature descriptors are aggregated for image retrieval [35], [36], and have successfully addressed most cases of loop closure detection in real-time Visual SLAM (VSLAM) [37], [38] without huge environmental changes.”
BackgroundReal-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video
Zhou, H., & Jayender, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927436 · Cited by 61
“SLAM is one of the most important topics in the robotics navigation field, which aims to estimate the camera motion and reconstruct the surrounding environment in real-time [5] [6].”
BackgroundMethodInfluentialLocal Supports Global: Deep Camera Relocalization With Sequence Enhancement
Xue, F., Wang, X., Yan, Z., Wang, Q., Wang, J., & Zha, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00293 · Cited by 62
“Visual Odometry Traditionally, visual odometry is solved by minimizing reprojection [21] or photometric errors [8, 9].”
BackgroundMethodInfluentialGraph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment
Gong, Z., Liu, P., Wen, F., Ying, R., Ji, X., Miao, R., & Xue, W. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3039640 · Cited by 62
“…odometry (VIO), and vision-based simultaneous localization and mapping (V-SLAM) systems under global navigation satellite system (GNSS)-denied environments have been extensively studied, which have produced fruitful outcomes, such as ORB-SLAM [1], [2], OKVIS [3], IVPR [4], and VINS-mono [5].”
BackgroundOA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM
Zins, M., Simon, G., & Berger, M. (2022) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar55827.2022.00090 · Cited by 62
“Current state-of-the-art SLAM methods, such as ORBSLAM2 [23], rely on bag-of-words descriptors to find similar images and local appearance-based features, such as ORB or SIFT, to find matches between keypoints detected in the query image and landmarks in the map.”
MethodC-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach
Millane, A., Taylor, Z., Oleynikova, H., Nieto, J. I., Siegwart, R., & Cadena, C. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593427 · Cited by 61
“to a set of visual features, before estimating the camera motion and the map as a function of only these feature observations [2].”
BackgroundMethodInfluentialWarren, M., Greeff, M., Patel, B., Collier, J. A., Schoellig, A. P., & Barfoot, T. D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2883408 · Cited by 61
Non-Rigid Volume to Surface Registration Using a Data-Driven Biomechanical Model
Pfeiffer, M., Riediger, C., Leger, S., Kühn, J., Seppelt, D., Hoffmann, R., Weitz, J., & Speidel, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_70 · Cited by 60
Vectorization for digital signal processors via equality saturation
VanHattum, A., Nigam, R., Lee, V. T., Bornholt, J., & Sampson, A. (2021) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3445814.3446707 · Cited by 62
“Other applications, such as simultaneous localization and mapping (SLAM) [20, 21, 33, 34] and structure from motion [35], have many components that are dominated entirely by small-scale kernels.”
BackgroundVisual-SLAM Classical Framework and Key Techniques: A Review
Jia, G., Li, X., Zhang, D., Xu, W., Lv, H., Shi, Y. C., & Cai, M. (2022) · Sensors · doi:10.3390/s22124582 · Cited by 64
“Figure 24 shows ORB-SLAM2 keyframes and the visibility graph.”
MethodEnabling human–machine collaboration in infrastructure inspections through mixed reality
Al‐Sabbag, Z. A., Yeum, C. M., & Narasimhan, S. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101709 · Cited by 60
CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments
Hu, X., Zhang, Y., Cao, Z., Ma, R., Wu, Y., Deng, Z., & Sun, W. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981826 · Cited by 60
“We contrast with ORB-SLAM2 [13] and forth most advanced dynamic SLAM methods, including DS-SLAM [4], Dyna-SLAM [5], Blitz-SLAM [12] and TRS [11].”
EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors
Yi, X., Zhou, Y., Habermann, M., Golyanik, V., Pan, S., Theobalt, C., & Xu, F. (2023) · ACM Transactions on Graphics · doi:10.1145/3592099 · Cited by 65
NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance
Pascoe, G., Maddern, W., Tanner, M., Piniés, P., & Newman, P. M. (2017) · 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.2017.158 · Cited by 58
“We compare the performance of NID-SLAM against two other state-of-the-art monocular SLAM approaches, ORBSLAM2 [23] and LSD-SLAM [7].”
MethodHierarchical Topic Model Based Object Association for Semantic SLAM
Zhang, J., Gui, M., Wang, Q., Liu, R., Xu, J., & Chen, S. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2932216 · Cited by 58
“The previous SLAM techniques rely on low-level landmarks, such as points [19, 33], lines [10, 23] and surface patches [21, 24].”
MethodLaidlow, T., Czarnowski, J., & Leutenegger, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793527 · Cited by 60
“With each new RGB image, the system obtains the pose from a monocular SLAM system (ORB-SLAM2 [3] in our implementation) and then updates the semi-dense depth estimates for the active keyframe using the method described in [28].”
MethodInfluentialSaD-SLAM: A Visual SLAM Based on Semantic and Depth Information
Yuan, X., & Chen, S. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341180 · Cited by 60
“In this paper, we propose a feature-based visual SLAM algorithm, which is capable of working robustly in dynamic environments, based on ORB-SLAM2 [1].”
BackgroundMethodInfluentialTian, F., Gao, Y., Fang, Z., Fang, Y., Gu, J., Fujita, H., & Hwang, J. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3080928 · Cited by 58
Deep Learning for Visual SLAM in Transportation Robotics: A review
Duan, C., Junginger, S., Huang, J., Jin, K., & Thurow, K. (2019) · Transportation Safety and Environment · doi:10.1093/tse/tdz019 · Cited by 59
“ORB-SLAM proposed by Mur-Artal et al. [17] inherits the framework of PTAM and replaces most of the modules, which is one of the most successful feature-based visual SLAM systems by now.”
MethodVision-guided unmanned aerial system for rapid multiple-type damage detection and localization
Jiang, S., Cheng, Y., & Zhang, J. (2022) · Structural Health Monitoring · doi:10.1177/14759217221084878 · Cited by 59
“Furthermore, the accuracy and speed of vision positioning were improved continuously with the development of noticeable algorithms include DSO,48 LSDSLAM,49 and ORB-SLAM2.”
Situational Graphs for Robot Navigation in Structured Indoor Environments
Bavle, H., Sánchez-López, J. L., Shaheer, M., Civera, J., & Voos, H. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3189785 · Cited by 59
Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping
Tian, C., Liu, H., Liu, Z., Li, H., & Wang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3243633 · Cited by 60
“Cartographer [12] is a classical laser SLAM algorithm based on graph optimization developed by Google.”
MethodSong, B., Yuan, X., Ying, Z., Yang, B., Song, Y. H., & Zhou, F. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3280533 · Cited by 58
“In the experiments, we thoroughly compared the proposed method with classical static SLAM algorithms (such as VINS-Mono [29], ORB-SLAM2 [5], and DSO [36]), the semantic dynamic SLAM system DS-SLAM [8], and the visual–inertial fusion SLAM system Dynamic_VINS [37].”
BackgroundMethodÇetinsaya, B., Reiners, D., & Cruz‐Neira, C. (2024) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2024.101626 · Cited by 58
UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery
Shetty, A., & Gao, G. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794228 · Cited by 58
“For visual odometry we implement the open-source ORBSLAM2 [1] package on our image sequence.”
BackgroundMethodThe UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments
Zuñiga-Noël, D., Jaenal, A., Gomez-Ojeda, R., & González-Jiménez, J. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920938439 · Cited by 58
“The evaluation shows that ORB_SLAM2 is hardly able to complete the sequences and PL-SLAM presents more robustness to visual tracking failure since it relies on both points and line segments features.”
MethodInfluentialDenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network
Gu, J., Xiang, Z., Ye, Y., & Wang, L. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3060396 · Cited by 58
“We choose open-source ORB-SLAM2 [37], a popular realtime SLAM library for Monocular [38], Stereo or RGB-D camera configurations, as our evaluation baseline.”
MethodToward the Distributed Implementation of Immersive Augmented Reality Architectures on 5G Networks
Morín, D. G., Pérez, P., & Armada, A. G. (2022) · IEEE Communications Magazine · doi:10.1109/mcom.001.2100225 · Cited by 57
UPLP-SLAM: Unified point-line-plane feature fusion for RGB-D visual SLAM
Yang, H., Yuan, J., Gao, Y., Sun, X., & Zhang, X. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.006 · Cited by 57
BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Autonomous Ground Vehicles
Luo, L., Cao, S.-Y., Xiaorui, L., Xu, J., Ai, R., Zhu, Y., & Chen, X. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3585385 · Cited by 59
“For example,insimultaneouslocalizationandmapping(SLAM) [1], [2], [3], [4], global localization provides loop closure constraints that reduce accumulative drifts and ensure globally consistent maps.”
Guided Feature Selection for Deep Visual Odometry
Xue, F., Wang, Q., Wang, X., Dong, W., Wang, J., & Zha, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20876-9_19 · Cited by 58
Communicating Multi-UAV System for Cooperative SLAM-based Exploration
Mahdoui, N., Frémont, V., & Natalizio, E. (2019) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-019-01062-6 · Cited by 57
“The ORB-SLAM2 approach [25] have been implemented 3 and evaluated along with the proposed exploration strategy.”
BackgroundMethodBenchmarking Classic and Learned Navigation in Complex 3D EnvironmentsPreprint
Mishkin, D., Dosovitskiy, A., & Koltun, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10915 · Cited by 57
A Survey on Radio Frequency based Precise Localisation Technology for UAV in GPS-denied Environment
Yang, B., & Yang, E. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01500-4 · Cited by 58
UAV Vision Aided INS/Odometer Integration for Land Vehicle Autonomous Navigation
Dong, J., Ren, X., Han, S., & Luo, S. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3151729 · Cited by 58
ORB-TEDM: An RGB-D SLAM Approach Fusing ORB Triangulation Estimates and Depth Measurements
Yuan, J., Zhu, S., Tang, K., & Sun, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3154800 · Cited by 56
Real-time indoor localization with visual SLAM for in-building emergency response
Tseng, P.-Y., Lin, J. J., Chan, Y.-C., & Chen, A. Y. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104319 · Cited by 58
A Practical Survey on Visual Odometry for Autonomous Driving in Challenging Scenarios and Conditions
Agostinho, L. R., Ricardo, N. M., Pereira, M. I., Hiolle, A., & Pinto, A. M. (2022) · IEEE Access · doi:10.1109/access.2022.3188990 · Cited by 57
DE‐SLAM: SLAM for highly dynamic environment
Xing, Z., Zhu, X., & Dong, D. (2022) · Journal of Field Robotics · doi:10.1002/rob.22062 · Cited by 56
Gao, C., Wang, X., Wang, R., Zhao, Z., Zhai, Y., Chen, X., & Chen, B. M. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104753 · Cited by 59
Li, C., & Guo, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.024 · Cited by 56
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
Won, C., Seok, H., Cui, Z., Pollefeys, M., & Lim, J. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196695 · Cited by 55
“First, we use the vocabulary tree [39] trained in [9], [29] for detecting loop closures.”
BackgroundMethodOn deep learning techniques to boost monocular depth estimation for autonomous navigation
Mendes, R. D. Q., Ribeiro, E. G., Rosa, N. D. S., & Grassi, V. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103701 · Cited by 57
Krátký, V., Petráček, P., Báča, T., & Saska, M. (2021) · Journal of Field Robotics · doi:10.1002/rob.22021 · Cited by 56
“Visual-inertial SLAM (VI-SLAM) [Cviˇsi´c et al., 2018, Zuo et al., 2020, Cadena et al., 2016, Mur-Artal and Tard´os, 2017] and visual-inertial odometry (VIO) [Delmerico and Scaramuzza, 2018] approaches estimate ego-motion from the image streams of a lightweight mono-or stereo-camera, enabling the…”
BackgroundDeepPilot: A CNN for Autonomous Drone Racing
Rojas-Perez, L. O., & Martínez-Carranza, J. (2020) · Sensors · doi:10.3390/s20164524 · Cited by 55
“…angle, and the planarity of the ground in the race track to define a ray-plane intersection formulation from which a synthetic depth image was generated and used within the RGB-D version of ORB-SLAM [14]; thus, waypoints where strategically placed for the drone to navigate and cross the gates.”
MethodZhang, J., Zhu, C., Zheng, L., & Xu, K. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459676 · Cited by 271
Visual Semantic Localization based on HD Map for Autonomous Vehicles in Urban Scenarios
Wang, H., Xue, C., Zhou, Y., Wen, F., & Zhang, H. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561459 · Cited by 55
“Mul-Artal [3] and Sons [16] built feature map with ORB and BIRD descriptor respectively, then the pose can be obtained by feature matching.”
MethodTwistSLAM: Constrained SLAM in Dynamic Environment
Gonzalez, M., Marchand, É., Kacete, A., & Royan, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3178150 · Cited by 55
“We also evaluate our approach on several sequences from the public KITTI dataset and compare our approach quantitatively with respect to ORB-SLAM2 [1], DynaSLAM [3], VDO-SLAM [6] and DynaSLAM II [5], which demonstrates the benefits of our method in terms of object poses and velocities and camera pose estimation accura…”
InfluentialResearch on Multi-View 3D Reconstruction Technology Based on SFM
Gao, L., Zhao, Y., Han, J., & Liu, H. (2022) · Sensors · doi:10.3390/s22124366 · Cited by 59
“The descriptor of SURF is based on the response of a 2D discrete wavelet transform and makes efficient use of the integral graph [24], which is more efficient and accurate than SIFT in running speed and brightness change; however, SIFT works better in the case of scale and rotation transformation, so the SIFT feature-…”
MethodNavigation without localisation: reliable teach and repeat based on the convergence theorem
Krajník, T., Majer, F., Halodová, L., & Vintr, T. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593803 · Cited by 52
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Rosinol, A., Sattler, T., Pollefeys, M., & Carlone, L. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794456 · Cited by 53
“In particular, featurebased methods [6]–[9] produce a point cloud that is not directly usable for path planning or obstacle avoidance.”
MethodBayer, J., & Faigl, J. (2019) · European Conference on Mobile Robots · doi:10.1109/ecmr.2019.8870968 · Cited by 56
“The state-ofthe-art feature-based visual localization ORB-SLAM2 [16] has been reported in several works [26], [27] as relatively accurate.”
BackgroundMethodInfluentialAutomatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors
Li, Y., Li, W., Tang, S., Darwish, W., Hu, Y., & Chen, W. (2020) · Sensors · doi:10.3390/s20010293 · Cited by 76
TagSLAM: Robust SLAM with Fiducial MarkersPreprint
Pfrommer, B., & Daniilidis, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00679 · Cited by 55
“Both VIO and ORB-SLAM2 exhibit drift, and evidently ORB-SLAM2 does not achieve loop closure.”
BackgroundInfluentialPrior Guided Dropout for Robust Visual Localization in Dynamic Environments
Huang, Z., Xu, Y., Shi, J., Zhou, X., Bao, H., & Zhang, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00288 · Cited by 54
“A notable solution for this problem is visual simultaneous localization and mapping (VSLAM) [32, 14, 13], which is able to simultaneously build the map and localize the camera by consecutively estimating relative pose transformation between image pairs.”
BackgroundZhao, C., Tang, Y. S., Sun, Q., & Vasilakos, A. V. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3071886 · Cited by 55
A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions
Yu, S., Fu, C., Gostar, A. K., & Hu, M. (2020) · Sensors · doi:10.3390/s20236988 · Cited by 55
“Sun et al. [61] employed the open source algorithm ORB_SLAM2 [60] to extract FAST key points [73] and binary robust independent elementary features (BRIEF) descriptors [74] for building sparse point cloud maps.”
MethodA Two-Stage Unsupervised Approach for Low Light Image Enhancement
Hu, J., Guo, X., Chen, J., Liang, G., Deng, F., & Lam, T. L. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3048667 · Cited by 54
Semantic visual SLAM in dynamic environment
Wen, S., Li, P., Zhao, Y., Zhang, H., Sun, F., & Wang, Z. (2021) · Autonomous Robots · doi:10.1007/s10514-021-09979-4 · Cited by 54
“Due to the poor robustness of ORB-SLAM2 for dynamic scenes, the performance of ORB-SLAM2 is somewhat different from the original paper in (Mur-Artal & Tard´os J D. Orb-slam2, 2017).”
MethodInfluentialA Real-Time Dynamic Object Segmentation Framework for SLAM System in Dynamic Scenes
Chang, J., Dong, N., & Li, D. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3109718 · Cited by 54
A General Framework for Lifelong Localization and Mapping in Changing Environment
Zhao, M., Guo, X., Song, L., Qin, B., Shi, X., Lee, G. H., & Sun, G. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635985 · Cited by 54
“A typical SLAM system consists of a front-end and a back-end module [5] [6] [7] [8].”
BackgroundAslan, M. F., Durdu, A., Yusefi, A., & Yılmaz, A. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.09.001 · Cited by 58
Stoiber, M., Sundermeyer, M., & Triebel, R. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00673 · Cited by 69
Sensing and Artificial Perception for Robots in Precision Forestry: A Survey
Ferreira, J. F., Portugal, D., Andrada, M. E., Machado, P., Rocha, R. P., & Peixoto, P. (2023) · Robotics · doi:10.3390/robotics12050139 · Cited by 60
Li, Y., Shen, H., Fu, Y., & Wang, K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124918 · Cited by 56
NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild
Ren, W., Zhu, Z., Sun, B., Chen, J., Pollefeys, M., & Peng, S. (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/cvpr52733.2024.00853 · Cited by 54
“Handling dynamic scenes has been studied for years in the literature of SLAM and SfM. Classical methods exclude pixels associated with dynamic objects with robust kernel function [8, 32] or RANSAC [41, 42].”
MethodLearnable Line Segment Descriptor for Visual SLAM
Vakhitov, A., & Lempitsky, V. (2019) · IEEE Access · doi:10.1109/access.2019.2901584 · Cited by 54
“The proposed ORB-SLAM2+LLD system outperforms the point-only system ORB-SLAM2 in all metrics except relative translation in one sequence out of three.”
MethodInfluentialExperimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles
Tschopp, F., Schneider, T., Palmer, A. W., Nourani‐Vatani, N., Cadena, C., Siegwart, R., & Nieto, J. I. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2897169 · Cited by 54
“1) ORB-SLAM2: Mur-Artal and Tardos [32] provide a complete visual SLAM system for monocular, stereo or RGB-D cameras called ORB-SLAM2.”
BackgroundMethodInfluentialROLS : Robust Object-Level SLAM for Grape Counting
Nellithimaru, A., & Kantor, G. A. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00321 · Cited by 54
“Current state-of-the-art SLAM systems like ORB-SLAM2 [ 13], LSD-SLAM [6] do not leverage semantic information.”
BackgroundFeature-based visual simultaneous localization and mapping: a survey
Azzam, R., Taha, T., Huang, S., & Zweiri, Y. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-2001-3 · Cited by 59
“Due to its dependence on visual features, ORB-SLAM2 fails in absence of ORB features in the scene.”
BackgroundMethodInfluentialSLAM for autonomous planetary rovers with global localization
Geromichalos, D., Azkarate, M., Tsardoulias, E., Gerdes, L., Petrou, L., & Pérez-del-Pulgar, C. (2020) · J. Field Robotics · doi:10.1002/rob.21943 · Cited by 53
Towards Resilient Autonomous Navigation of Drones
Santamaria‐Navarro, À., Thakker, R., Fan, D. D., Morrell, B. J., & Agha–mohammadi, A. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-95459-8_57 · Cited by 54
DymSLAM: 4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
Wang, C., Luo, B., Zhang, Y., Zhao, Q. Z., Yin, L., Wang, W., Su, X. B., Wang, Y., & Li, C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3045647 · Cited by 54
“where λ is the quantized length – in our experiments, ∀λ ∈ [1, 50] and usually equals to 1 when being used in dynamic SLAM systems.”
MethodInfluentialZeng, T., Tang, F., Ji, D., & Si, B. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.02.023 · Cited by 53
Segmenting areas of potential contamination for adaptive robotic disinfection in built environments
Hu, D., Zhong, H., Li, S., Tan, J., & He, Q. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107226 · Cited by 56
Karpyshev, P., Ilin, V., Kalinov, I., Petrovsky, A., & Tsetserukou, D. (2021) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/ieeeconf49454.2021.9382649 · Cited by 54
“The authors of ORB-SLAM also proposed a second version of their algorithm: ORB-SLAM2 [32].”
BackgroundDifferentiable SLAM-net: Learning Particle SLAM for Visual Navigation
Karkus, P., Cai, S., & Hsu, D. (2021) · 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/cvpr46437.2021.00284 · Cited by 53
Niu, X., Liu, T., Kuang, J., Zhang, Q., & Guo, C. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3113398 · Cited by 53
“map of an unknown indoor environment, and localize the pedestrian in the map [11], [12].”
BackgroundFast ORB-SLAM Without Keypoint Descriptors
Fu, Q., Yu, H., Wang, X., Yang, Z., He, Y., Zhang, H., & Mian, A. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3136710 · Cited by 55
“Our method achieves SOTA performance when compared with ORB-SLAM2 [1], ElasticFusion [48], Kintinuous [49], DVO-SLAM [13], RGBD-SLAM2 [14], BundleFusion [15], BAD-SLAM [16], Lei et al.”
BackgroundMethodInfluentialWei, R., Li, B., Mo, H., Lu, B., Long, Y., Yang, B., Dou, Q., Liu, Y., & Sun, D. B. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3195027 · Cited by 54
“(a) For each data, the second column is the reconstruction result of our method and the last column is the result of ORB-SLAM2 [14], [38].”
MethodMetaStream: Live Volumetric Content Capture, Creation, Delivery, and Rendering in Real Time
Guan, Y., Hou, X., Wu, N., Han, B., & Han, T. (2023) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3570361.3592530 · Cited by 53
“On smart cameras, we implement the dynamic camera calibration module based on ORB feature extraction and tracking [58], OpenCV [23], and RealSense SDK [5]; we implement the segmentation and streaming with RealSense SDK [5], OpenCV [23], Nvidia Docker [12], TensorFlow [20], and x264 library [19]; the segmentation model…”
BackgroundMethodInfluentialIslam, Q. U., Ibrahim, H., Chin, P. K., Lim, K., Abdullah, M. Z., & Khozaei, F. (2024) · Displays · doi:10.1016/j.displa.2024.102654 · Cited by 53
RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM With Neural Radiance Fields
Jiang, H., Xu, Y., Li, K., Feng, J., & Zhang, L. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3427554 · Cited by 61
“Traditional visual SLAM methods using semantic segmentation prior [2]–[5], optical flow motion [6]–[8] or re-sampling and residual optimization strategies [9]–[11] to remove the outliers under dynamic environments, which can improve the accuracy and robustness of pose estimation.”
MethodHBST: A Hamming Distance Embedding Binary Search Tree for Feature-Based Visual Place Recognition
Schlegel, D., & Grisetti, G. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2856542 · Cited by 53
“BOW and VLAD approaches are reported to be both robust and efficient [22]–[24] however they do not provide point correspondences, that are required for geometric verification.”
BackgroundMethodInfluentialCPFG-SLAM:a Robust Simultaneous Localization and Mapping based on LIDAR in Off-Road Environment
Ji, K., Chen, H., Di, H., Gong, J., Xiong, G., Qi, J., & Yi, T. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500599 · Cited by 53
Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments
Feigl, T., Porada, A., Steiner, S. J., Löffler, C., Mutschler, C., & Philippsen, M. (2020) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0008989903070318 · Cited by 53
“, 2017) or RGB-D (Mur-Artal and Tardos, 2017; Kerl et al., 2013) signals with inertial sensor data from the headset (Kasyanov et al.”
BackgroundMethodInfluentialMulti-camera visual SLAM for off-road navigation
Yang, Y. M., Tang, D., Wang, D., Song, W., Wang, J., & Fu, M. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103505 · Cited by 55
Multipath Computation Offloading for Mobile Augmented Reality
Braud, T., Zhou, P., Kangasharju, J., & Hui, P. (2020) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom45495.2020.9127360 · Cited by 54
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
Garg, S., Harwood, B., Anand, G., & Milford, M. J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3005627 · Cited by 56
“It is worth noting that a subsequent geometric verification step, commonly employed in hierarchical localization pipelines [39], [49], [50], can further improve the precision performance.”
BackgroundMethodReal-Time Object Navigation With Deep Neural Networks and Hierarchical Reinforcement Learning
Staroverov, A., Yudin, D. A., Belkin, I., Adeshkin, V., Solomentsev, Y., & Panov, A. I. (2020) · IEEE Access · doi:10.1109/access.2020.3034524 · Cited by 53
“In the course of the article, both classical approaches with SLAM (ORB-SLAM2) [11] and planning (D * Lite), and the neural network approach (Direct Future Prediction) [12] were analyzed in different noise conditions with different types of sensors.”
MethodA Benchmark for Visual-Inertial Odometry Systems Employing Onboard Illumination
Kasper, M., McGuire, S., & Heckman, C. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968554 · Cited by 52
“Both the stereo and monocular versions of OKVIS exhibit very little drift, while monocular ORBSLAM and DSO’s poor performance is largely the result of extreme scale drift.”
MethodInfluentialComparison of modern open-source Visual SLAM approaches
Sharafutdinov, D., Griguletskii, M., Kopanev, P., Kurenkov, M., Ferrer, G., Бурков, А. В., Gonnochenko, A., & Tsetserukou, D. (2023) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-023-01812-7 · Cited by 55
“But during the paper writing, it has been deleted from the GitHub 2 due to the concerns on similarities of the source code to the ORB-SLAM2.”
BackgroundMethodInfluential
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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