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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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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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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
Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972
“The most notable examples are SIFT (Scale-Invariant Feature Transform) (44, 45), BRISK (Binary Robust Invariant Scalable Keypoints) (46), SURF (Speeded Up Robust Features) (47, 48), and ORB (Oriented FAST and Rotated BRIEF) (49, 50).”
BackgroundInfluentialDynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Bescos, B., Fácil, J. M., Civera, J., & Neira, J. (2018) · Zaguan (Universidad de Zaragoza) · Cited by 893
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
Zhu, Z., Peng, S., Larsson, V., Xu, W., Bao, H., Cui, Z., Oswald, M. R., & Pollefeys, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01245 · Cited by 917
“As can be noticed, the state- of-the-art approaches for tracking (e.g. BAD-SLAM [42], ORB-SLAM2 [26]) still outperform the methods based on implicit scene representations (iMAP [46] and ours).”
MethodVisual-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
“The base of our visual-inertial system is ORB-SLAM [12], [20].”
BackgroundSelf-citationKITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
Liao, Y., Xie, J., & Geiger, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179507 · Cited by 770
“As ORB-SLAM2 does not provide dense reconstruction nor semantic information, we obtain dense semantic reconstruction by unprojecting 2D semantic seg-mentations (PSPNet [116]) using depth maps from semi-global matching (SGM) [42].”
MethodInfluentialPerspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
“Such spatial reconstructions of the environment can either be extracted by using dedicated sensors such as LiDAR (120) or can be reconstructed from multiple images, either by stitching the images into a unified two-dimensional panorama (e.g., mosaicking (121)) or by computing the three-dimensional environment from par…”
BackgroundVisual SLAM algorithms: a survey from 2010 to 2016
Taketomi, T., Uchiyama, H., & Ikeda, S. (2017) · IPSJ Transactions on Computer Vision and Applications · doi:10.1186/s41074-017-0027-2 · Cited by 755
“ORB-SLAM is extended to the stereo vSLAM and the RGB-D vSLAM [39].”
BackgroundiMAP: Implicit Mapping and Positioning in Real-Time
Sucar, E., Liu, S., Ortiz, J., & Davison, A. J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00617 · Cited by 748
“We compare with surfel-based BAD-SLAM [24], TSDF fusion Kintinuous [38], and sparse ORB-SLAM2 [16], state-of-the-art SLAM systems.”
MethodPL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments
Gomez-Ojeda, R., Moreno, F.-Á., Zuñiga-Noël, D., Scaramuzza, D., & González-Jiménez, J. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2899783 · Cited by 578
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
Sun, K., Mohta, K., Pfrommer, B., Watterson, M., Liu, S., Mulgaonkar, Y., Taylor, C. J., & Kumar, V. R. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2793349 · Cited by 542
Matsuki, H., Murai, R., Kelly, P. H. J., & Davison, A. J. (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/cvpr52733.2024.01708 · Cited by 644
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review
Fayyad, J., Jaradat, M. A., Gruyer, D., & Najjaran, H. (2020) · Sensors · doi:10.3390/s20154220 · Cited by 559
Probabilistic data association for semantic SLAM
Bowman, S. L., Atanasov, N., Daniilidis, K., & Pappas, G. J. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989203 · Cited by 482
“The trajectories estimated by our algorithm, by the ROVIO visual-inertial odometry algorithm [31], and by the ORB-SLAM2 visual SLAM algorithm [44], [45], projected into the x-y plane, are shown in Fig.”
MethodSplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Keetha, N., Karhade, J., Murthy, J. K., Yang, G., Scherer, S., Ramanan, D., & Luiten, J. (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/cvpr52733.2024.02018 · Cited by 576
“For the past three decades, SLAM research has extensively centered around the question of map representation – resulting in a variety of sparse [2, 3, 7, 23], dense [4, 6, 8, 13, 15, 25, 26, 34, 42, 43], and neural scene representations [21, 29, 30, 37, 46, 55].”
BackgroundMethodComputing Systems for Autonomous Driving: State of the Art and Challenges
Liu, L., Lu, S., Ren, Z. G., Wu, B., Yao, Y., Zhang, Q., & Shi, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3043716 · Cited by 496
“Featurebased methods [122]–[124] extract features and track them to calculate the motion of the camera.”
MethodBayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
Yang, N., von Stumberg, L., Wang, R., & Cremers, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00136 · Cited by 438
“In particular, DVSO outperforms other monocular VO systems by a large margin, and even achieves comparable performance to state-of-the-art stereo visual odometry systems [53,74].”
BackgroundA Comprehensive Survey of Visual SLAM Algorithms
Barros, A. M., Michel, M., Moline, Y., Corre, G., & Carrel, F. (2022) · Robotics · doi:10.3390/robotics11010024 · Cited by 444
“The Visual-Inertial ORB-SLAM (VIORB) algorithm [71] is based on the already presented ORB-SLAM algorithm [44].”
MethodA Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots
Delmerico, J., & Scaramuzza, D. (2018) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2018.8460664 · Cited by 421
“• SVO+GTSAM [8] - a lightweight visual odometry frontend with a full-smoothing backend provided by iSAM2 [9] We do not consider non-inertial visual simultaneous localization and mapping (SLAM) systems, for example ORBSLAM [10] and LSD-SLAM [11].”
BackgroundMethodStereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras
Wang, R., Schwörer, M., & Cremers, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.421 · Cited by 406
“More recently, ORB-SLAM [21, 22] introduced an efficient visual SLAM solution based on ORB features and innovations such as map reuse.”
MethodA survey of state-of-the-art on visual SLAM
Kazerouni, I. A., Fitzgerald, L., Dooly, G., & Toal, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117734 · Cited by 420
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM
Schöps, T., Sattler, T., & Pollefeys, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00022 · Cited by 413
“Results for other methods are as reported in [7], [70] and [44].”
MethodInfluentialA Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping
Debeunne, C., & Vivet, D. (2020) · Sensors · doi:10.3390/s20072068 · Cited by 406
“Such research works generated semi-dense maps representing the environment, but direct SLAM approaches are time consuming and often require GPU based processing.”
BackgroundSantos, T. T., de Souza, L. L., dos Santos, A. A., & Avila, S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105247 · Cited by 375
“A fast SLAM algorithm such as ORB-SLAM (Mur-Artal & Tardos, 2017) or SVO (Forster et al., 2014), not relying on any specific landmark, could be a more robust alternative.”
MethodDeep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
Yang, N., Wang, R., Stückler, J., & Cremers, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_50 · Cited by 346
Xiao, L., Wang, J., Qiu, X., Zheng, R. Y., & Zou, X. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.012 · Cited by 350
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
LDSO: Direct Sparse Odometry with Loop Closure
Gao, X., Wang, R., Demmel, N., & Cremers, D. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593376 · Cited by 340
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM
Bescos, B., Campos, C., Tardós, J. D., & Neira, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3068640 · Cited by 343
“DynaSLAM II builds on the popular ORB-SLAM2 [1].”
BackgroundBrossard, M., Barrau, A., & Bonnabel, S. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.2980758 · Cited by 338
“Our approach solely based on the IMU produces accurate estimates and competes with top-ranked LiDAR and stereo camera methods [6,7]; and we do not know of IMU based dead-reckoning methods capable to compete with such results; • the approach is not restricted to inertial only dead-reckoning of…”
BackgroundMethodA Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers
Placed, J. A., Strader, J., Carrillo, H., Atanasov, N., Indelman, V., Carlone, L., & Castellanos, J. A. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3248510 · Cited by 345
“[66] ORB-SLAM2 [67] Lidar & RGB-D Octomap Modular Frontiers Graph’s D-opt Simulation Time ✓”
BackgroundIntensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Wang, H., Wang, C., & Xie, L. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196764 · Cited by 348
“1) Temporal consistency check: In a SLAM system, it is observed that the occurrence of single loop closure often implies high similarity on the neighbour LiDAR scans since the sensor feedback is continuous in time [25].”
MethodInfluentialPL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features
He, Y., Zhao, J. Y., Guo, Y., He, W., & Yuan, K. (2018) · Sensors · doi:10.3390/s18041159 · Cited by 331
Detect-SLAM: Making Object Detection and SLAM Mutually Beneficial
Zhong, F., Wang, S., Zhang, Z., Chen, C., & Wang, Y. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00115 · Cited by 323
“4.1 shows some examples of computed trajectories of our system using the TUM dataset, with comparison to the ground-truth and the original RGB-D-based ORB-SLAM [19].”
MethodInfluentialQuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM
Nicholson, L., Milford, M. J., & Sünderhauf, N. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2866205 · Cited by 337
“This is supported by further experiments that demonstrated it was possible to improve the trajectory error compared to ORB-SLAM2 on a number of sequences by rejecting objects with high bounding box variance.”
BackgroundMethodInfluentialRDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods
Liu, Y., & Miura, J. (2021) · IEEE Access · doi:10.1109/access.2021.3050617 · Cited by 326
“Mur-Artal et al. presented ORB-SLAM2 [16], a complete SLAM system for monocular, stereo, and RGB-D cameras, which works in real-time on standard CPUs in a wide variety of environments.”
BackgroundMethodA Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles
Zou, Q., Sun, Q., Chen, L., Nie, B., & Li, Q. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3063477 · Cited by 321
“For loop-closure detection, methods based on the bag-of-words (BoW) technique were popular used [17], [19], [36], where the accumulative error can largely be eliminated by optimizing a pose graph.”
MethodFusion++: Volumetric Object-Level SLAM
McCormac, J., Clark, R., Bloesch, M., Davison, A. J., & Leutenegger, S. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00015 · Cited by 317
Sumikura, S., Shibuya, M., & Sakurada, K. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350539 · Cited by 314
“In particular, ORB–SLAM [10, 11], LSD–SLAM [4], and DSO [3] constitute major approaches regarded as de facto standards of visual SLAM, which performs SLAM processing using imagery.”
BackgroundMethodInfluentialR $^2$ LIVE: A Robust, Real-Time, LiDAR-Inertial-Visual Tightly-Coupled State Estimator and Mapping
Lin, J., Zheng, C., Xu, W. D., & Zhang, F. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3095515 · Cited by 304
A Review of Multi-Sensor Fusion SLAM Systems Based on 3D LIDAR
Xu, X., Zhang, L., Yang, J., Cao, C.-F., Wang, W., Ran, Y.-Y., Tan, Z.-Y., & Luo, M.-Z. (2022) · Remote Sensing · doi:10.3390/rs14122835 · Cited by 290
“The system is built on ORB-SLAM2 [43] and uses Livox to provide a more accurate depth estimation for Remote Sens. 2022, 14, 2835 9 of 27 the camera.”
MethodInfluentialNeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
Rosinol, A., Leonard, J. J., & Carlone, L. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341922 · Cited by 362
“VolBA uses a direct RGB loss for pose estimation and mapping, while Orbeez-SLAM and Abou-Chakra et al. use ORB-SLAM [32] to provide the initial poses.”
MethodLow Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead
Yue, P., An, J., Zhang, J., Ye, J., Pan, G., Wang, S., Xiao, P., & Hanzo, L. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3296160 · Cited by 333
“Therefore, a pair of salient features of CV-aided schemes, which are beneficial for the security of LEO SCSs, can be summarized as follow: 1) One-way sensing capability: By employing optical cameras/devices [357], no RF signals have to be transmitted and received, hence resulting both in low”
Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools
Jiang, S., Jiang, C., & Jiang, W. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.04.016 · Cited by 292
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
Wang, H., Wang, J., & Agapito, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01277 · Cited by 287
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
Palazzolo, E., Behley, J., Lottes, P., Giguère, P., & Stachniss, C. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967590 · Cited by 282
“[1] combine a geometric approach with a deep learning segmentation to enable removal of dynamics in an ORB-SLAM2 system [8].”
BackgroundKimera: From SLAM to spatial perception with 3D dynamic scene graphs
Rosinol, A., Violette, A., Abate, M., Hughes, N., Chang, Y., Shi, J., Gupta, A., & Carlone, L. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211056674 · Cited by 306
“2018) also uses the deformation graph approach to deform the model point cloud using the estimate from ORB-SLAM (Mur-Artal and Tardós 2017).”
MethodIndoor navigation: state of the art and future trends
El‐Sheimy, N., & Li, Y. (2021) · Satellite Navigation · doi:10.1186/s43020-021-00041-3 · Cited by 285
“For the former, the research work (Mur-Artal and Tardós 2017) can support visual SLAM using monocular, stereo, and Red–Green–Blue-Depth (RGB-D) cameras.”
MethodYOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint
Wu, W., Guo, L., Gao, H., You, Z., Liu, Y., & Chen, Z. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06764-3 · Cited by 272
“Some advanced visual SLAM systems have achieved pretty decent performances, such as ORB-SLAM2 [10], LSD-SLAM [11], RGBD-SLAM-V2 [12].”
BackgroundMaplab: An Open Framework for Research in Visual-Inertial Mapping and Localization
Schneider, T., Dymczyk, M., Fehr, M., Egger, K., Lynen, S., Gilitschenski, I., & Siegwart, R. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800113 · Cited by 265
“Table II shows timing information of ROVIO and ROVIOLI compared to ORB-SLAM2 [25].”
BackgroundResultNeural-Guided RANSAC: Learning Where to Sample Model Hypotheses
Brachmann, E., & Rother, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00442 · Cited by 269
“ite its simplicity and time of invention, Random Sample Consensus (RANSAC) [12] remains an important method for robust optimization, and is a vital component of many state-of-the-art vision pipelines [39,40,29,6]. RANSAC allows accurate estimation of model parameters from a set of observations of which some are outlie…”
MethodRGB-D SLAM in Dynamic Environments Using Point Correlations
Dai, W.-C., Zhang, Y., Li, P., Fang, Z., & Scherer, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3010942 · Cited by 257
“Factor-graph-optimization-based methods can be further divided into two categories: indirect (feature-based) methods [17], [18], [19] and direct methods [20], [21], [22].”
MethodASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer
Chen, H., Luo, Z., Zhou, L., Tian, Y., Zhen, M., Fang, T., McKinnon, D. H., Tsin, Y., & Quan, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19824-3_2 · Cited by 265
Hu, M., Yin, W., Zhang, C., Cai, Z., Long, X., Chen, H., Wang, K., Yu, G., Shen, C., & Shen, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3444912 · Cited by 423
“These unique attributes render both depth and surface normals indispensable in various computer vision applications, including 3D reconstruction [1], [2], [3], neural rendering (NeRF) [4], [5], [6], [7], autonomous driving [8], [9], [10], and robotics [11], [12], [13].”
FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry
Zheng, C., Zhu, Q., Xu, W., Liu, X., Guo, Q., & Zhang, F. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981107 · Cited by 257
“Currently, there are several successful implemented framework using a single measurement sensor such as camera [1]–[3] or LiDAR [4]–[6].”
MethodESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
Johari, M. M., Carta, C., & Fleuret, F. (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/cvpr52729.2023.01670 · Cited by 260
“While traditional SLAM systems [16, 41, 45, 55, 76, 77] mostly focus on localization accuracy, recent learning-based dense visual SLAM methods [2,11,25,35,60,64,65, 67, 81, 86] provide meaningful global 3D maps and show reasonable but limited reconstruction accuracy.”
A review of visual SLAM methods for autonomous driving vehicles
Cheng, J. T., Zhang, L., Chen, Q., Hu, X., & Cai, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104992 · Cited by 267
Point-SLAM: Dense Neural Point Cloud-based SLAM
Sandström, E., Li, Y., Gool, L. V., & Oswald, M. R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01690 · Cited by 271
The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth
Ramezani, M., Wang, Y., Camurri, M., Wisth, D., Mattamala, M., & Fallon, M. F. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340849 · Cited by 248
“We tested ORB-SLAM2 [28] for basic stereo odometry with loop closures disabled.”
MethodEvent-Based Stereo Visual Odometry
Zhou, Y. B., Gallego, G., & Shen, S. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3062252 · Cited by 254
“Additionally, we compare against a stateof-the-art frame-based SLAM pipeline (ORB-SLAM2 [67]) running on the grayscale frames acquired by the stereo DAVIS.”
MethodVox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation
Yang, X., Li, H., Zhai, H., Ming, Y., Liu, Y., & Zhang, G. (2022) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar55827.2022.00066 · Cited by 243
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
Yin, W., Zhang, C., Chen, H., Cai, Z., Yu, G., Wang, K., Chen, X., & Shen, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00830 · Cited by 305
An integrated UGV-UAV system for construction site data collection
Asadi, K., Suresh, A. K., Ender, A., Gotad, S., Maniyar, S., Anand, S., Noghabaei, M., Han, K. K., Lobaton, E. J., & Wu, T. (2020) · Automation in Construction · doi:10.1016/j.autcon.2019.103068 · Cited by 243
Unsupervised Traffic Accident Detection in First-Person Videos
Yao, Y., Xu, M., Wang, Y., Crandall, D., & Atkins, E. M. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967556 · Cited by 231
“0 [33] for ego odometry calculation and compute optical flow using FlowNet 2.”
MethodLearning the Depths of Moving People by Watching Frozen People
Li, Z., Dekel, T., Cole, F. H., Tucker, R. P., Snavely, N., Liu, C., & Freeman, W. T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00465 · Cited by 226
“[53], we use ORB-SLAM2 [24] to identify trackable sequences in each video and to estimate an initial camera pose for each frame.”
MethodLimo: Lidar-monocular visual odometry
Graeter, J., Wilczynski, A., & Lauer, M. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594394 · Cited by 261
ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction
Zhao, X., Wu, X., Miao, J., Chen, W., Chen, P. C. Y., & Li, Z. G. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3155927 · Cited by 233
“Hence they are widely used in real-time visual applications like simultaneous localization and mapping (SLAM) systems [2], [3], and high dynamic range imaging (HDRI) [4], [5].”
BackgroundHuang, H., Li, L., Cheng, H., & Yeung, S.-K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02039 · Cited by 266
“For example, ORB-SLAM series meth-ods [2, 23, 24] rely on extracting and tracking lightweight geometric features across consecutive frames, which perform bundle adjustment locally instead of globally.”
StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments
Scona, R., Jaimez, M., Pétillot, Y., Fallon, M. F., & Cremers, D. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460681 · Cited by 220
“For ORB-SLAM [12], [13] the authors enforce an effective survival-of-the-fittest strategy which judges the validity of keyframes and the points used for pose tracking.”
MethodIntensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Wang, H., Chen, W., & Xie, L. (2020) · IEEE Conference Proceedings · Cited by 222
FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow
Zhang, T., Zhang, H., Li, Y., Nakamura, Y., & Zhang, L. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197349 · Cited by 220
“in [10] applied SegNet [11] to detect and remove foreground humans and then estimate the camera motions with ORB-SLAM2 [12] framework.”
MethodSLAM; definition and evolution
Taheri, H., & Xia, Z. C. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.104032 · Cited by 242
ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera MotionPreprint
Zhang, J., Zhu, C., Zheng, L., & Xu, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05600 · Cited by 217
Unsupervised Scale-Consistent Depth Learning from Video
Bian, J.-W., Zhan, H., Wang, N., Li, Z., Zhang, L., Shen, C., Cheng, M., & Reid, I. D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01484-6 · Cited by 218
“The camera pose is optimized through the Bundle Adjustment (Mur-Artal & Tardós 2017).”
BackgroundMethodInfluentialBlitz-SLAM: A semantic SLAM in dynamic environments
Fan, Y., Zhang, Q., Tang, Y., Liu, S., & Han, H. W. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108225 · Cited by 213
An Overview on Visual SLAM: From Tradition to Semantic
Chen, W.-F., Shang, G., Ji, A.-H., Zhou, C., Wang, X., Xu, C., Li, Z., & Hu, K. (2022) · Remote Sensing · doi:10.3390/rs14133010 · Cited by 217
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Pillai, S., Ambruş, R., & Gaidon, A. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793621 · Cited by 214
“[39], [13]) and limit our quantitative comparison to similar self-supervised learning based methods.”
Data-Efficient Decentralized Visual SLAM
Cieslewski, T., Choudhary, S., & Scaramuzza, D. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461155 · Cited by 204
Zhang, J., Qian, S., & Tan, C. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105225 · Cited by 207
Visual Odometry Revisited: What Should Be Learnt?
Zhan, H., Weerasekera, C. S., Bian, J.-W., & Reid, I. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197374 · Cited by 201
“One of the most successful and accurate full SLAM systems using a sparse (ORB) feature-based approach is ORB-SLAM2 [13], along with DSO [16], a direct keyframe-based sparse SLAM method.”
MethodInfluentialMotion removal for reliable RGB-D SLAM in dynamic environments
Sun, Y., Liu, M., & Meng, M. Q. ‐. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.07.002 · Cited by 200
FAST-LIVO2: Fast, Direct LiDAR–Inertial–Visual Odometry
Zheng, C., Xu, W. D., Zou, Z., Hua, T., Yuan, C., He, D., Zhou, B., Liu, Z., Lin, J., Zhu, F., et al. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3502198 · Cited by 203
“Currently, several SLAM frameworks have been successfully implemented with single-measurement sensors, primarily cameras [1], [2], [3], [4], or LiDAR [5], [6], [7].”
Robotic weed control using automated weed and crop classification
Wu, X., Aravecchia, S., Lottes, P., Stachniss, C., & Pradalier, C. (2020) · Journal of Field Robotics · doi:10.1002/rob.21938 · Cited by 196
“Indirect methods, (Klein and Murray, 2007) (Mur-Artal and Tardós, 2017), pre-process acquired an image to extract a certain type of features, which is utilized to recover the camera pose and scene structure by minimizing geometric error.”
BackgroundMethodWhere Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning
Wellhausen, L., Dosovitskiy, A., Ranftl, R., Walas, K., Cadena, C., & Hutter, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2895390 · Cited by 195
“For tracking the camera pose we use a visual SLAM system ORB-SLAM2 [19].”
MethodS-PTAM: Stereo Parallel Tracking and Mapping
Pire, T., Fischer, T., Castro, G. I., Cristóforis, P., Civera, J., & Jacobo-Berlles, J. (2017) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.03.019 · Cited by 187
Ravi Raj, K. G., & Kos, A. (2022) · Applied Sciences · doi:10.3390/app12146951 · Cited by 202
“2D, 3D LiDAR, and stereo vision sensors [73].”
Georgiou, T., Liu, Y., Chen, W., & Lew, M. S. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00183-w · Cited by 206
TransVPR: Transformer-Based Place Recognition with Multi-Level Attention Aggregation
Wang, R., Shen, Y., Zuo, W., Zhou, S., & Zheng, N. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01328 · Cited by 190
“In early VPR systems [1, 11, 25, 30, 31, 46], traditional methods such as SIFT [26], SURF [4] and ORB [39] have been widely used to represent a small patch centered around a detected keypoint.”
MethodGCNv2: Efficient Correspondence Prediction for Real-Time SLAM
Tang, J., Ericson, L., Folkesson, J. B., & Jensfelt, P. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2927954 · Cited by 188
“this category is still defined by ORB-SLAM2 [7], [15].”
BackgroundGoal-Driven Autonomous Exploration Through Deep Reinforcement Learning
Cimurs, R., Suh, I. H., & Lee, J. H. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3133591 · Cited by 187
A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision
Morar, A., Moldoveanu, A. D. B., Mocanu, I. G., Moldoveanu, F., Rǎdoi, E., Asavei, V., Grădinaru, A., & Butean, A. (2020) · Sensors · doi:10.3390/s20092641 · Cited by 189
“Our proposed classification and the selection of localization solutions for each category aimed to allow the reader to easily grasp the advantages and applicability of each class of solutions.”
BackgroundAre We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
Shi, X., Li, D., Zhao, P., Tian, Q., Tian, Y., Long, Q., Zhu, C., Song, J., Qiao, F., Song, L., et al. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9196638 · Cited by 185
“Then ATE of each matched ORB_SLAM2 [16] (stereo fisheye) ORB_SLAM2 [16] (RGB-D) DS-SLAM [18] (RGB-D) DSO [17] (RGB) VINS-Mono [19] (RGB+IMU) VINS-Mono [19] (fisheye+IMU) InfiniTAMv2 [20] (RGB-D) ElasticFusion [ The percentage value on the top left of each scene is average Correct Rate (CR ε , φ as…”
BackgroundTowards Better Generalization: Joint Depth-Pose Learning Without PoseNet
Zhao, W., Liu, S., Shu, Y., & Liu, Y. (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/cvpr42600.2020.00917 · Cited by 186
“earning systems. Although our dense correspondence is learned in an unsupervised manner and no local BA and mapping are used at inference, we achieve comparable results with conventional SLAM systems [39]. Figure5shows the recovered trajectories on two tested sequences respectively. 4.4. Generalization on Unseen Ego-m…”
MethodInfluentialRecasens, D., Lamarca, J., Fácil, J. M., Montiel, J. M. M., & Civera, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3095528 · Cited by 196
Ekanayake, B., Wong, J. K. W., Fini, A. A. F., & Smith, P. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103705 · Cited by 194
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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