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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
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DeepMatcher: A deep transformer-based network for robust and accurate local feature matching
Xie, T., Dai, K., Wang, K., Li, R., & Zhao, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121361 · Cited by 86
DN-SLAM: A Visual SLAM With ORB Features and NeRF Mapping in Dynamic Environments
Ruan, C., Zang, Q., Zhang, K., & Huang, K. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3345877 · Cited by 81
“While Orbeez-SLAM [14] uses ORB-SLAM2 [34] to provide the initial pose, combining implicit neural representations and visual odometry, none of these studies deal well with dynamic environments, and the accuracy of camera poses obtained from their dynamic environments decreases significantly, as…”
MethodLearning Multi-View Camera Relocalization With Graph Neural Networks
Xue, F., Wu, X., Cai, S., & Wang, J. (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/cvpr42600.2020.01139 · Cited by 79
“Therefore, the loss function is close to the standard pose-graph [7], which has been an indispensable component of current reconstruction and SLAM systems [26, 32] to improve the pose accuracy.”
BackgroundMethodSurvey and Evaluation of RGB-D SLAM
Zhang, S., Zheng, L., & Tao, W. (2021) · IEEE Access · doi:10.1109/access.2021.3053188 · Cited by 78
“For the indirect method, we select ORB-SLAM2 [3] as the comparison object, since it is the most classic and representative indirect-method-based RGB-D SLAM, and it is also the universal basis of many subsequent systems.”
MethodInfluentialKrul, S., Pantos, C., Frangulea, M., & Valente, J. R. P. (2021) · Drones · doi:10.3390/drones5020041 · Cited by 78
“According to the criteria, LSD-SLAM [39] was found to meet the criteria for the direct methods, and ORB-SLAM in the implementation of ORB-SLAM2 [40] for the feature-based methods.”
BackgroundMethodInfluentialHa, S., Yeon, J., & Yu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72764-1_11 · Cited by 86
Feng, T., & Gu, D. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2925555 · Cited by 78
“ESP-VO [24] SfMLearner [5] ORB-SLAM [25] UndeepVO [9] Undepthflow [11] (416 × 128) for next temporal window until all the frames in the data set are used.”
Method3-D Canonical Pose Estimation and Abnormal Gait Recognition With a Single RGB-D Camera
Guo, Y., Deligianni, F., Gu, X., & Yang, G. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2928775 · Cited by 77
“the camera estimated via ORB-SLAM [15] and RGB-D images.”
InfluentialLagoon water quality monitoring based on digital image analysis and machine learning estimators
Li, Y., Wang, X., Zhao, Z., Han, S., & Liu, Z. (2020) · Water Research · doi:10.1016/j.watres.2020.115471 · Cited by 77
Li, G., Yu, L., & Fei, S. (2020) · Measurement · doi:10.1016/j.measurement.2020.108403 · Cited by 80
SLAM Overview: From Single Sensor to Heterogeneous Fusion
Chen, W., Zhou, C., Shang, G., Wang, X., Li, Z., Xu, C., & Hu, K. (2022) · Remote Sensing · doi:10.3390/rs14236033 · Cited by 88
A Survey of Visual SLAM in Dynamic Environment: The Evolution From Geometric to Semantic Approaches
Wang, Y., Tian, Y., Chen, J., Xu, K., & Ding, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3420374 · Cited by 79
Challenges in Autonomous UAV Cinematography: An Overview
Mademlis, I., Mygdalis, V., Nikolaidis, N., & Pitas, I. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486586 · Cited by 77
Geometric Correspondence Network for Camera Motion Estimation
Tang, J., Folkesson, J. B., & Jensfelt, P. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2794624 · Cited by 76
“This is similar to [3] where alignment of images is performed in both orders to utilize as much information as possible from them.”
BackgroundMethodAssigning Visual Words to Places for Loop Closure Detection
Tsintotas, K. A., Bampis, L., & Gasteratos, A. C. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461146 · Cited by 75
“The off-line appearance-based method proposed in [15] utilizes the BoW model for image representation, while a Chow Liu tree [16] learns the co-visibility probabilities between VWs’ occurrences.”
MethodMemon, A. R., Wang, H., & Hussain, A. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103470 · Cited by 75
Plane-Edge-SLAM: Seamless Fusion of Planes and Edges for SLAM in Indoor Environments
Sun, Q., Yuan, J., Zhang, X., & Duan, F. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3032831 · Cited by 76
Liu, Y., & Miura, J. (2021) · IEEE Access · doi:10.1109/access.2021.3100426 · Cited by 78
BIM-based indoor mobile robot initialization for construction automation using object detection
Zhao, X., & Cheah, C. C. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104647 · Cited by 76
UnsuperPoint: End-to-end Unsupervised Interest Point Detector and DescriptorPreprint
Christiansen, P., Kragh, M. F., Brodskiy, Y., & Karstoft, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04011 · Cited by 90
“Bundle adjustment enables large-scale applications (long sequence recordings in large scenes) [21, 22], correction of maps based on loop closure [23], and fusion with odometry sensors such as GPS [24] or as in Visual-Inertial SLAM [25, 26], where an IMU is used to reduce drift and improve…”
BackgroundRobust Visual Localization in Dynamic Environments Based on Sparse Motion Removal
Cheng, J., Wang, C., & Meng, M. Q. ‐. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2940543 · Cited by 75
“In implementation, we use ORB-SLAM [23] as the basic localization scheme.”
BackgroundMethodResultInfluentialDDL-SLAM: A Robust RGB-D SLAM in Dynamic Environments Combined With Deep Learning
Ai, Y., Rui, T., Lü, M., Fu, L., Liu, S., & Wang, S. (2020) · IEEE Access · doi:10.1109/access.2020.2991441 · Cited by 77
“Our goal is to enhance the robustness and stability of RGB-D SLAM based on ORB-SLAM2 [33] in highly-dynamic scenarios.”
BackgroundWF-SLAM: A Robust VSLAM for Dynamic Scenarios via Weighted Features
Zhong, Y., Hu, S., Huang, G., Bai, L., & Li, Q. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3169340 · Cited by 75
Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot
Yang, L., Li, B., Li, W., Brand, H., Jiang, B., & Xiao, J. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003234 · Cited by 75
“Meanwhile, the concrete defects prediction output is registered to 3D space using the depth information and the target defect’s 3D location and surface normal [21] are used to navigate the CityFlyer to the best viewing angle.”
MethodLiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods
Fu, B., Wang, Y., Ding, X., Jiao, Y., Tang, L., & Xiong, R. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2931526 · Cited by 75
“First, we run ORB-SLAM [29] using the acquired camera image to obtain the pose of the camera at each moment and the position of the 3D points in the map.”
MethodRNNSLAM: Reconstructing the 3D colon to visualize missing regions during a colonoscopy
Ma, R., Wang, R., Zhang, Y., Pizer, S. M., McGill, S., Rosenman, J. G., & Frahm, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102100 · Cited by 76
Yan, L., Hu, X., Zhao, L., Chen, Y., Wei, P., & Xie, H. (2022) · Remote Sensing · doi:10.3390/rs14030795 · Cited by 75
“Due to the increasing amount of attention from scholars in the past few decades, many VSLAM systems with good performance have been developed, such as LSD-SLAM [6], DSO [7], and ORB-SLAM2 [8].”
BackgroundMethodInfluentialAzpúrua, H., da Silva, M. S., Freitas, G., Clark, L., Agha–mohammadi, A., Pessin, G., Campos, M. F. M., & Macharet, D. G. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104304 · Cited by 74
ASRO-DIO: Active Subspace Random Optimization Based Depth Inertial Odometry
Zhang, J., Tang, Y., Wang, H., & Xu, K. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3208503 · Cited by 74
Ongoing Evolution of Visual SLAM from Geometry to Deep Learning: Challenges and Opportunities
Li, R., Wang, S., & Gu, D. (2018) · Cognitive Computation · doi:10.1007/s12559-018-9591-8 · Cited by 80
Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments
Fan, D. D., Thakker, R., Bartlett, T., Miled, M. B., Kim, L., Theodorou, E., & Agha–mohammadi, A. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968276 · Cited by 71
“ORB-SLAM2 is an open-source SLAM system for monocular, stereo, and RGBD cameras which leverages the speed of ORB features and back-end bundle adjustment to enable lightweight and accurate real-time localization [30].”
BackgroundRGB-D Semantic Segmentation and Label-Oriented Voxelgrid Fusion for Accurate 3D Semantic Mapping
Shi, W., Xu, J., Zhu, D., Zhang, G., Wang, X., Li, J., & Zhang, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3056726 · Cited by 73
CNN-Based Dense Monocular Visual SLAM for Real-Time UAV Exploration in Emergency Conditions
Steenbeek, A., & Nex, F. (2022) · Drones · doi:10.3390/drones6030079 · Cited by 74
“The SLAM system is based on ORB SLAM 2 [24], which combines well-known ORB [40] descriptors and FAST detectors [41] in an image pyramid structure to allow for a more reliable tracking of the features.”
BackgroundMethodAerial orthoimage generation for UAV remote sensing: Review
Zhang, J., Xu, S., Zhao, Y., Sun, J., Xu, S., & Zhang, X. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.007 · Cited by 76
From SLAM to Situational Awareness: Challenges and Survey
Bavle, H., Sánchez-López, J. L., Cimarelli, C., Tourani, A., & Voos, H. (2023) · Sensors · doi:10.3390/s23104849 · Cited by 88
RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions
Hong, Z., Pétillot, Y., Wallace, A. M., & Wang, S. (2022) · The International Journal of Robotics Research · doi:10.1177/02783649221080483 · Cited by 92
“Since ORB-SLAM2 fails to produce meaningful results due to the visual degradation caused by water drops, blurry effects in low-light conditions and occlusion from snow (see Figure 13 for example images), its results are not reported in this section.”
BackgroundMethodInfluentialA Novel Lidar-Assisted Monocular Visual SLAM Framework for Mobile Robots in Outdoor Environments
Yin, J., Luo, D., Yan, F., & Zhuang, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3190031 · Cited by 74
Automated wall‐climbing robot for concrete construction inspection
Yang, L., Li, B., Feng, J., Yang, G., Yong, C., Jiang, B., & Xiao, J. (2022) · Journal of Field Robotics · doi:10.1002/rob.22119 · Cited by 72
Learning how to robustly estimate camera pose in endoscopic videos
Hayoz, M., Hahne, C., Gallardo, M., Candinas, D., Kurmann, T., Allan, M., & Sznitman, R. (2023) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-023-02919-w · Cited by 72
“Well-established techniques such as ORB-SLAM2 [1] and ElasticFusion [2] have shown great promise in rigid scenes.”
BackgroundMethodInfluentialShariq, M. H., & Hughes, B. R. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109979 · Cited by 71
Wall-climbing robot for non-destructive evaluation using impact-echo and metric learning SVM
Li, B., Ushiroda, K., Yang, L., Song, Q., & Xiao, J. (2017) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-017-0028-4 · Cited by 71
Pseudo RGB-D for Self-improving Monocular SLAM and Depth Prediction
Tiwari, L., Ji, P., Tran, Q.-H., Zhuang, B., Anand, S., & Chandraker, M. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_26 · Cited by 70
Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
Yu, H., Zhen, W., Yang, W. T., Zhang, J., & Scherer, S. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341690 · Cited by 70
“Cameras and IMUs are low-cost, light-weight and commonly available sensors and current visual-inertial based pose estimation and mapping methods are well-developed for a variety of robot systems [1], [2], [3].”
BackgroundMethodYokozuka, M., Koide, K., Oishi, S., & Banno, A. (2020) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros45743.2020.9341341 · Cited by 59
“LiDAR odometry and mapping (LOAM) [5] is an early feature-based method with LiDAR-based SLAM; it is similar to feature-based visual SLAM, such as ORB-SLAM [10].”
MethodI Can See the Light: Attacks on Autonomous Vehicles Using Invisible Lights
Wang, W., Yao, Y., Liu, X., Li, X., Hao, P., & Zhu, T. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484766 · Cited by 70
“The AV utilizes the most commonly used ORB-SLAM2 architecture [5, 51] to perform the SLAM process.”
Methodmaplab 2.0 – A Modular and Multi-Modal Mapping Framework
Cramariuc, A., Bernreiter, L., Tschopp, F., Fehr, M., Reijgwart, V., Nieto, J. I., Siegwart, R., & Cadena, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3227865 · Cited by 71
“ORBSLAM3 is an extension of its predecessor ORB-SLAM2 [15], adding support for an IMU and multi-session mapping capabilities.”
BackgroundAn Overview of Key SLAM Technologies for Underwater Scenes
Wang, X., Fan, X., Shi, P., Ni, J., & Zhou, Z. (2023) · Remote Sensing · doi:10.3390/rs15102496 · Cited by 70
A Robust RGB-D SLAM System With Points and Lines for Low Texture Indoor Environments
Fu, Q., Yu, H., Lai, L., Wang, J., Xia, P., Sun, W. J., & Sun, M. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2927405 · Cited by 70
“In this section, we introduce the loop closure and mapping thread, which is built on the work of RGBD SLAM-v2 [9] ORB SLAM2 [10], and Bags of Words (BoWs) [37].”
BackgroundMethodInfluentialPanoramic SLAM from a multiple fisheye camera rig
Ji, S., Qin, Z., Shan, J., & Lü, M. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.014 · Cited by 70
Enhanced UAV Indoor Navigation through SLAM-Augmented UWB Localization
Tiemann, J., Ramsey, A., & Wietfeld, C. (2018) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2018.8403539 · Cited by 71
“monocular [21], stereo and RGB-D cameras [22] developed by Mur-Artal et.”
BackgroundDecoupling Makes Weakly Supervised Local Feature Better
Li, K., Wang, L., Liu, L., Ran, Q., Xu, K., & Guo, Y. (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/cvpr52688.2022.01538 · Cited by 69
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis: A Review
Dalal, A., Hagen, D., Robbersmyr, K. G., & Knausgård, K. M. (2024) · IEEE Access · doi:10.1109/access.2024.3408318 · Cited by 88
“This involves incorporating parameters like 3D position, rotation, scaling factor, and spherical harmonics coefficients for color and opacity.”
MethodDRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot
Yang, D., Bi, S., Wang, W., Chang, Y., Wang, W., Qi, X., & Cai, Y. (2019) · Remote Sensing · doi:10.3390/rs11040380 · Cited by 68
“DynaSLAM [9] extends the capabilities of ORB-SLAM2 [1] to build static dense point cloud maps in dynamic environments.”
BackgroundMethodMin, H., Wu, X., Cheng, C., & Zhao, X. (2019) · Sensors · doi:10.3390/s19245430 · Cited by 69
Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Views
Cartillier, V., Ren, Z., Jain, N., Lee, S., Essa, I. A., & Batra, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16180 · Cited by 68
Chen, Z., Liu, X., Kojima, M., Huang, Q., & Arai, T. (2021) · Sensors · doi:10.3390/s21041536 · Cited by 76
“Visual SLAM, including Monocular, Stereo, RGB-D cameras, and Lidars, has shown significant advantages in intelligent mobile robots and autonomous vehicles because of robustness, intelligence, and Some SLAM schemes, such as ORB-SLAM [7,8], LSD-SLAM [9], ElasticFusion [10], and SVO [11], are mature. adaption.”
BackgroundInfluentialA LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization
Chen, S., Zhou, B., Jiang, C., Xue, W., & Li, Q. (2021) · Remote Sensing · doi:10.3390/rs13142720 · Cited by 68
“Representative open-source algorithms include Karto SLAM [7], and Cartographer [9], etc.”
MethodAutoMine: An Unmanned Mine Dataset
Li, Y., Li, Z., Teng, S., Zhang, Y., Zhou, Y., Zhu, Y., Cao, D., Tian, B., Ai, Y., Xuanyuan, Z., et al. (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/cvpr52688.2022.02062 · Cited by 70
A Dynamic Service-Oriented Software Architecture for Highly Automated Vehicles
Kampmann, A., Alrifaee, B., Kohout, M., Wustenberg, A., Woopen, T., Nolte, M., Eckstein, L., & Kowalewski, S. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8916841 · Cited by 74
“The innovation cycles of these technologies tend to be short, which becomes evident in the rapid progress that has been made in the areas of image recognition [8], LIDAR processing [9], localization [10] and other perception and control systems in the recent years.”
BackgroundMasked GAN for Unsupervised Depth and Pose Prediction With Scale Consistency
Zhao, C., Yen, G. G., Sun, Q., Zhang, C., & Tang, Y. S. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3044181 · Cited by 67
Line Flow Based Simultaneous Localization and Mapping
Wang, Q., Yan, Z., Wang, J., Xue, F., Ma, W., & Zha, H. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3061403 · Cited by 67
Topology Aware Object-Level Semantic Mapping Towards More Robust Loop Closure
Lin, S., Wang, J., Xu, M., Zhao, H., & Chen, Z. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3097242 · Cited by 67
“Traditional appearance-based loop detection methods [2], [3] model the environment as a set of images captured in the process of mapping, and scene is recognized according to the similarity between images, which is performed by local feature extraction and matching [4].”
MethodMarshall, J. A., Sun, W., & L’Afflitto, A. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.10.013 · Cited by 71
MVS‐SLAM: Enhanced multiview geometry for improved semantic RGBD SLAM in dynamic environment
Islam, Q. U., Ibrahim, H., Chin, P. K., Lim, K., & Abdullah, M. Z. (2023) · Journal of Field Robotics · doi:10.1002/rob.22248 · Cited by 70
A comprehensive overview of core modules in visual SLAM framework
Cai, D., Li, R., Hu, Z., Lu, J., Li, S., & Zhao, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127760 · Cited by 67
Huang, W., & Liu, H. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460206 · Cited by 68
“Although the proposed method is implemented based on the monocular ORB-SLAM [20], [21], our method is generic and could be applied to any keyframe-based visual odometry or SLAM system.”
MethodPSPNet-SLAM: A Semantic SLAM Detect Dynamic Object by Pyramid Scene Parsing Network
Long, X., Zhang, W., & Zhao, B. (2020) · IEEE Access · doi:10.1109/access.2020.3041038 · Cited by 67
“…has attracted a large number of researchers with emergence of many excellent SLAM system frameworks such as MonoSLAM [1], ORB-SLAM [2], ORB-SLAM2 [3], LSD-SLAM [4], SVO [5], DynaSLAM [6], which can achieve satisfactory performance while mobile robots are used in a static environment or some…”
BackgroundMethodDV-LOAM: Direct Visual LiDAR Odometry and Mapping
Wang, W., Liu, J., Wang, C., Luo, B., & Zhang, C. (2021) · Remote Sensing · doi:10.3390/rs13163340 · Cited by 58
“It is worth mentioning that this similar strategy is widely used in various SLAM methods, including direct [11,12] and indirect methods [8,9].”
BackgroundMethodResultEfficient and Consistent Bundle Adjustment on Lidar Point Clouds
Liu, Z., Liu, X., & Zhang, F. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3311671 · Cited by 76
“Compared to visual BA, which has been well-established in photogrammetry and played a fundamental role in various vital applications, including visual odometry (VO) [23], [24], [25], visual-inertial odometry (VIO) [26], [27], 3-”
MethodActive SLAM: A Review on Last Decade
Ahmed, M. F., Masood, K., Frémont, V., & Fantoni, I. (2023) · Sensors · doi:10.3390/s23198097 · Cited by 68
“Each agent can choose to perform its independent ORB-SLAM [103] or localize other agents.”
BackgroundInfluentialTowards the industry 5.0 frontier: Review and prospect of XR in product assembly
Wang, B., Zheng, L., Wang, Y., Fang, W., & Wang, L. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.05.002 · Cited by 66
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Bodin, B., Wagstaff, H., Saecdi, S., Nardi, L., Vespa, E., Mawer, J., Nisbet, A. P., Luján, M., Furber, S. B., Davison, A. J., et al. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460558 · Cited by 66
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
von Stumberg, L., Wenzel, P., Khan, Q., & Cremers, D. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965031 · Cited by 63
“Available: https://vision.in.tum.de/gn-net scheme implemented in ORB-SLAM for relocalization, however, it yielded worse results overall.”
BackgroundMethodInfluentialNavigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment
Chiang, K., Tsai, G.-J., Li, Y.-H., Li, Y., & El‐Sheimy, N. (2020) · Remote Sensing · doi:10.3390/rs12101564 · Cited by 65
Mahmood, B., Han, S., & Lee, D.-E. (2020) · Remote Sensing · doi:10.3390/rs12142302 · Cited by 64
“, SIFT [27], SURF [28], BRISK [30], or ORB-SLAM2 [36], can also be used for registration with additional processes of accurate transformation estimation for an image to an image or an image to a point cloud.”
BackgroundMethodVisual SLAM in dynamic environments based on object detection
Ai, Y., Rui, T., Yang, X., He, J., Fu, L., Li, J., & Lu, M. (2020) · Defence Technology · doi:10.1016/j.dt.2020.09.012 · Cited by 66
Real-Time Dynamic SLAM Algorithm Based on Deep Learning
Su, P., Luo, S., & Huang, X. (2022) · IEEE Access · doi:10.1109/access.2022.3199350 · Cited by 70
“Mur-Artal et al. [7] proposed the ORB-SLAM2 system, which is the most classic visual SLAM system based on the feature point method.”
MethodSDV-LOAM: Semi-Direct Visual–LiDAR Odometry and Mapping
Yuan, Z., Wang, Q., Cheng, K., Hao, T., & Yang, X. Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3262817 · Cited by 66
“Visual odometry [1], [2], [3], [4], [5], [6], [7] can output pose and map points at a high frequency (i.e., 30-60 Hz), but the accuracy is not as good as LiDAR odometry due to its poor robustness to blurred images and low textures.”
BackgroundMethodProSLAM: Graph SLAM from a Programmer's Perspective
Schlegel, D., Colosi, M., & Grisetti, G. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461180 · Cited by 67
“From the translation error we can tell that our approach stands in the comparable range with the competing methods, outperforming LSD-SLAM in 7/11 and ORB-SLAM2 in 3/11 cases.”
BackgroundMethodInfluentialHopkinson, B. M., King, A. C., Owen, D. P., Johnson‐Roberson, M., Long, M. H., & Bhandarkar, S. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230671 · Cited by 68
“computational complexity of the reconstruction problem and increasing the potential spatial coverage and accuracy [49, 54].”
BackgroundXia, L., Cui, J., Shen, R., Xu, X. W., Gao, Y., & Li, X. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420919185 · Cited by 70
“…segmentation and moving consistency check to eliminate ORB feature points that initially exist in a dynamic object, which not only outperforms ORB-SLAM2 63 regarding accuracy and robustness in a dynamic environment but also builds a dense semantic octo-tree map for further 3D representation.”
Explaining Autonomous Driving by Learning End-to-End Visual Attention
Cultrera, L., Seidenari, L., Becattini, F., Pala, P., & Bimbo, A. D. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00178 · Cited by 71
“be able to observe and understand the surrounding scene. If data acquisition can rely on a vast array of sensors [17], the understanding process must involve sophisticated machine learning algorithms [10, 20, 5, 33, 39]. Once the vehicle is able to perceive the environment, it must learn to navigate under strict const…”
BackgroundRobust Underwater Visual SLAM Fusing Acoustic Sensing
Vargas, E., Scona, R., Willners, J. S., Łuczyński, T., Cao, Y., Wang, S., & Pétillot, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561537 · Cited by 64
“[9] use ORB-SLAM2 [10] with stereo cameras to demonstrate dense 3-D mapping of underwater caves, producing maps that are of higher resolution than those obtained using acoustic sensors.”
MethodBinocular video-based 3D reconstruction and length quantification of cracks in concrete structures
Deng, L., Sun, T., Yang, L., & Cao, R. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104743 · Cited by 65
Lin, J., & Zhang, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3456473 · Cited by 71
“In [39], LiDAR measurements were used to provide depth information for camera images at each frame, forming a system similar to RGB-D camera and hencebeingabletoleverageexistingvisualSLAMworkssuchasORB-SLAM2[29].”
MethodLiDAR, IMU, and camera fusion for simultaneous localization and mapping: a systematic review
Fan, Z., Zhang, L., Wang, X., Shen, Y., & Deng, F. Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11187-w · Cited by 87
Loosely-Coupled Semi-Direct Monocular SLAM
Lee, S. H., & Civera, J. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2889156 · Cited by 71
“Afterwards, a full BA [30] optimizes all keyframes and feature points in the map for global consistency.”
BackgroundAn evaluation of ROS-compatible stereo visual SLAM methods on a nVidia Jetson TX2
Giubilato, R., Chiodini, S., Pertile, M., & Debei, S. (2019) · Measurement · doi:10.1016/j.measurement.2019.03.038 · Cited by 62
Global Localization with Object-Level Semantics and Topology
Liu, Y., Pétillot, Y., Lane, D. M., & Wang, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794475 · Cited by 63
Unsupervised Learning of Depth, Optical Flow and Pose With Occlusion From 3D Geometry
Wang, G., Zhang, C., Wang, H., Wang, J., Wang, Y., & Wang, X. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3010418 · Cited by 63
Drivocopter: A concept Hybrid Aerial/Ground vehicle for long-endurance mobility
Kalantari, A., Touma, T., Kim, L., Jitosho, R., Strickland, K., Lopez, B. T., & Agha–mohammadi, A. (2020) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero47225.2020.9172782 · Cited by 63
“We utilize the image stream of the Intel Realsense camera and ORB-SLAM2, an open source system for SLAM [18], to provide pose estimates which are then fused with IMU data via an Extended”
MethodIntroducing autonomous aerial robots in industrial manufacturing
Pérez-Grau, F. J., Dios, J. R. M., Paneque, J. L., Acevedo, J. J., Torres‐González, A., Viguria, A., Astorga, J. R., & Ollero, A. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.06.008 · Cited by 69
SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World
Stoiber, M., Pfanne, M., Strobl, K. H., Triebel, R., & Albu‐Schäffer, A. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01579-8 · Cited by 63
“…compare SRT3D to state-of-the-art region-based tracking approaches, as well as an approach from Bugaev et al. (2018) and prominent methods such as PWP3D (Prisacariu and Reid, 2012), ElasticFusion (Whelan et al., 2015), UDP (Brachmann et al., 2016), and ORB-SLAM2 (Mur-Artal and Tardós, 2017).”
MethodSOFT2: Stereo Visual Odometry for Road Vehicles Based on a Point-to-Epipolar-Line Metric
Cvišić, I., Marković, I., & Petrovic, I. K. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3188121 · Cited by 66
“For reference, we also process every sequence with the publicly available ORB-SLAM2 [10], OV2SLAM [33], and VINS-FUSION [11], [43] implementations, and compare the results to SOFT2.”
MethodResultAirVO: An Illumination-Robust Point-Line Visual Odometry
Xu, K., Hao, Y., Yuan, S., Wang, C., & Xie, L. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341914 · Cited by 64
“We also add stereo-mode VINS-Fusion [3], ORB-SLAM2 [17], Basalt-VIO [39] and VIO-mode OKVIS [49] to the baselines.”
MethodEdge-Assisted Multi-Robot Visual-Inertial SLAM With Efficient Communication
Liu, X., Wen, S., Zhao, J., Qiu, T. Z., & Zhang, H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3376427 · Cited by 64
“VI ORB-SLAM [35] adds IMU information to assist visual measurement based on [20], [21], and ORB (Oriented FAST and Rotated BRIEF) [36] feature is adopted for feature extraction.”
InfluentialWalking Posture Adaptation for Legged Robot Navigation in Confined Spaces
Buchanan, R., Bandyopadhyay, T., Bjelonic, M., Wellhausen, L., Hutter, M., & Kottege, N. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2899664 · Cited by 63
Loosely-Coupled Semi-Direct Monocular SLAM
Lee, S. H., & Civera, J. (2019) · Zaguan (University of Zaragoza Repository) · Cited by 63
A Survey on Visual Navigation and Positioning for Autonomous UUVs
Qin, J., Li, M., Li, D., Zhong, J., & Yang, K. (2022) · Remote Sensing · doi:10.3390/rs14153794 · Cited by 65
“Tightly-coupled methods combine the data of the vision sensor and the IMU through optimization and filtering to jointly construct the motion equation, so as to obtain the final pose information by considering the state estimation of the two kinds of data.”
MethodMeasuring robustness of Visual SLAM
Prokhorov, D., Zhukov, D., Barinova, O., Konushin, A., & Vorontsova, A. (2019) · Machine Vision Applications · doi:10.23919/mva.2019.8758020 · Cited by 62
“We have tried different implementations of the recent SLAM methods and have chosen state-of-the-art ORBSLAM2 [9] due to it’s performance and stability of the implementation (1).”
BackgroundMethodResultInfluential
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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