The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
MonoSLAM: Real-Time Single Camera SLAM
Abstract
We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For example, UVIO [146] employs a transformer backbone to jointly learn from visual and IMU inputs, significantly improving pose estimation in dynamic environments.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [146]
Citations
Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724
Lucas-Kanade 20 Years On: A Unifying Framework
Baker, S., & Matthews, I. A. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000011205.11775.fd · Cited by 3,159
A solution to the simultaneous localization and map building (SLAM) problem
Dissanayake, M. W. M. G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.938381 · Cited by 2,661
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2002) · doi:10.5555/777092.777184 · Cited by 2,085
Real-time simultaneous localisation and mapping with a single camera
Davison (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238654 · Cited by 1,749
Nistér, D., Naroditsky, O., & Bergen, J. R. (2004) · Cited by 1,169
Nistér, D., Naroditsky, O., & Bergen, J. R. (2004) · 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.2004.1315094 · Cited by 1,031
Directed Sonar Sensing for Mobile Robot Navigation
Leonard, J. J., & Durrant-Whyte, H. F. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3652-9 · Cited by 798
Inverse Depth Parametrization for Monocular SLAM
Civera, J., Davison, A. J., & Montiel, J. M. M. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2003276 · Cited by 768
Self-citation
Flagged first, then by citations
Research works printed but not linked: 6
- Moutarlier, Stochastic Multisensory Data Fusion for Mobile Robot Location and Environement Modelling
- Neira, Mobile Robot Localisation and Map Building Using Monocular Vision
- 10.1007/BFb0054781 · DOI
- Sim, Vision-Based SLAM Using the Rao-Blackwellised Particle Filter
- Chiuso, "MFm": 3-D Motion from 2-D Motion Causally Integrated over Time
- Molton, Parameterisation and Probability in Image Alignment
Cited by
Sorted by citations
Items 301–400 of 1,001
Bae, S., Agarwala, A., & Durand, F. (2010) · ACM Transactions on Graphics · doi:10.1145/1805964.1805968 · Cited by 89
Edge Robotics: Edge-Computing-Accelerated Multirobot Simultaneous Localization and Mapping
Huang, P., Zeng, L., Chen, X., Huang, L., Zhou, Z., & Yu, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3146461 · Cited by 88
Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression
Anderson, S., Barfoot, T. D., Tong, C. H., & Särkkä, S. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9455-y · Cited by 87
An Indoor Wayfinding System Based on Geometric Features Aided Graph SLAM for the Visually Impaired
Zhang, H., & Ye, C. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2682265 · Cited by 87
FPGA based Speeded Up Robust Features
Šváb, J., Krajník, T., Faigl, J., & Přeučil, L. (2009) · IEEE International Conference on Technologies for Practical Robot Applications · doi:10.1109/tepra.2009.5339646 · Cited by 86
Linear RGB-D SLAM for Planar Environments
Kim, P., Coltin, B., & Kim, H. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_21 · Cited by 86
A framework for global vehicle localization using stereo images and satellite and road maps
Senlet, T., & Elgammal, A. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130498 · Cited by 86
FPGA-based ORB feature extraction for real-time visual SLAM
Fang, W., Zhang, Y., Bo, Y., & Liu, S. (2017) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2017.8280159 · Cited by 85
Realtime multibody visual SLAM with a smoothly moving monocular camera
Kundu, A., Krishna, K. M., & Jawahar, C. V. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126482 · Cited by 85
Efficient probabilistic Range-Only SLAM
Blanco, J., Fernández‐Madrigal, J., & González, J. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650650 · Cited by 85
Kinecting the dots: Particle based scene flow from depth sensors
Hadfield, S., & Bowden, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126509 · Cited by 85
Tightly coupled integrated navigation system via factor graph for UAV indoor localization
Yang, S. S., & Hsu, L. (2020) · Aerospace Science and Technology · doi:10.1016/j.ast.2020.106370 · Cited by 84
The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology
Fu, Z., Jin, Z., Zhang, C., He, Z., Zha, Z., Hu, C., Gan, T., Yan, Q., Wang, P., & Ye, X. (2021) · IEEE Access · doi:10.1109/access.2021.3065104 · Cited by 84
Visual map matching and localization using a global feature map
Pink, O. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563135 · Cited by 84
An efficient multiple hypothesis filter for bearing-only SLAM
Kwok, N. M., & Dissanayake, G. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389440 · Cited by 84
Incremental vision-based topological SLAM
Angeli, A., Doncieux, S., Meyer, J.-A., & Filliat, D. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650675 · Cited by 84
High Accuracy Monocular SFM and Scale Correction for Autonomous Driving
Song, S., Chandraker, M., & Guest, C. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2469274 · Cited by 84
Keyframe-based real-time camera tracking
Dong, Z., Zhang, G., Jia, J., & Bao, H. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459273 · Cited by 84
Webb, B. H. (2019) · Journal of Experimental Biology · doi:10.1242/jeb.188094 · Cited by 87
Enabling physical analytics in retail stores using smart glasses
Rallapalli, S., Ganesan, A., Chintalapudi, K. K., Padmanabhan, V. N., & Qiu, L. (2014) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2639108.2639126 · Cited by 83
Feature Co-occurrence Maps: Appearance-based localisation throughout the day
Johns, E., & Yang, G. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631024 · Cited by 83
An architecture for robust UAV navigation in GPS‐denied areas
Pérez-Grau, F. J., Ragel, R., Caballero, F., Viguria, A., & Ollero, A. (2017) · Journal of Field Robotics · doi:10.1002/rob.21757 · Cited by 83
Urban 3D modeling using mobile laser scanning: a review
Wang, C., Wen, C., Dai, Y., Yu, S., & Liu, M. (2020) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2020.05.003 · Cited by 82
Scene perception based visual navigation of mobile robot in indoor environment
Ran, T., Yuan, L., & Zhang, J. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.10.023 · Cited by 83
Hybrid Feature Tracking and User Interaction for Markerless Augmented Reality
Lee, T., & Höllerer, T. (2008) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2008.4480766 · Cited by 82
Fusing Monocular Information in Multicamera SLAM
Solà, J., Monin, A., Devy, M., & Vidal‐Calleja, T. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004640 · Cited by 82
A visual odometry framework robust to motion blur
Pretto, A., Menegatti, E., Bennewitz, M., Burgard, W., & Pagello, E. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152447 · Cited by 82
High‐speed autonomous obstacle avoidance with pushbroom stereo
Barry, A. J., Florence, P., & Tedrake, R. (2017) · Journal of Field Robotics · doi:10.1002/rob.21741 · Cited by 82
Fast loop-closure detection using visual-word-vectors from image sequences
Bampis, L., Amanatiadis, A. A., & Gasteratos, A. C. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917740639 · Cited by 81
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
Deep Learning Techniques for Visual SLAM: A Survey
Mokssit, S., Licea, D. B., Guermah, B., & Ghogho, M. (2023) · IEEE Access · doi:10.1109/access.2023.3249661 · Cited by 81
Monocular Vision SLAM for Indoor Aerial Vehicles
Çelik, K., & Somani, A. K. (2013) · Journal of Electrical and Computer Engineering · doi:10.1155/2013/374165 · Cited by 81
Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity
Huang, S., Kwok, N. M., Dissanayake, G., Ha, Q. P., & Fang, G. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570261 · Cited by 81
Observer Design for Stochastic Nonlinear Systems via Contraction-Based Incremental Stability
Dani, A. P., Chung, S., & Hutchinson, S. (2014) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2357671 · Cited by 81
Visual SLAM for Flying Vehicles
Steder, B., Grisetti, G., Stachniss, C., & Burgard, W. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004521 · Cited by 81
Memory management for real-time appearance-based loop closure detection
Labbé, M., & Michaud, F. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094602 · Cited by 80
Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey
Sualeh, M., & Kim, G.-W. (2018) · International Journal of Control Automation and Systems · doi:10.1007/s12555-018-0130-x · Cited by 85
Li, G., Yu, L., & Fei, S. (2020) · Measurement · doi:10.1016/j.measurement.2020.108403 · Cited by 80
Indoor localization and visualization using a human-operated backpack system
Liu, T., Carlberg, M. A., Chen, G., Chen, J. H. K., Kua, J., & Zakhor, A. (2010) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2010.5646820 · Cited by 80
Localization for Multirobot Formations in Indoor Environment
Chen, H., Sun, D. B., Yang, J., & Chen, J. (2009) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2009.2030584 · Cited by 80
Perception-driven navigation: Active visual SLAM for robotic area coverage
Kim, A., & Eustice, R. M. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631022 · Cited by 80
Visual topological SLAM and global localization
Angeli, A., Doncieux, S., Meyer, J.-A., & Filliat, D. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152501 · Cited by 84
Sanfilippo, F., Azpiazu, J., Marafioti, G., Transeth, A. A., Stavdahl, Ø., & Liljebäck, P. (2017) · Applied Sciences · doi:10.3390/app7040336 · Cited by 79
Visual navigation of a mobile robot with laser-based collision avoidance
Cherubini, A., & Chaumette, F. (2013) · Int. J. Robotics Res. · doi:10.1177/0278364912460413 · Cited by 91
Lin, B., Sun, Y., Qian, X., Goldgof, D., Gitlin, R., & You, Y. (2016) · The international journal of medical robotics + computer assisted surgery : MRCAS · doi:10.1002/rcs.1661 · Cited by 87
RANGE - robust autonomous navigation in GPS-denied environments
Bachrach, A., de Winter, A., He, R., Hemann, G., Prentice, S., & Roy, N. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509990 · Cited by 79
Vision-Based Estimation for Guidance, Navigation, and Control of an Aerial Vehicle
Kaiser, M. K., Gans, N., & Dixon, W. E. (2010) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2010.5545174 · Cited by 78
EKF monocular SLAM with relocalization for laparoscopic sequences
Grasa, Ó. G., Civera, J., & Montiel, J. M. M. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980059 · Cited by 78
Experimental Comparison of Open Source Vision-Based State Estimation Algorithms
Li, A. Q., Coskun, A., Doherty, S. M., Ghasemlou, S., Jagtap, A., Modasshir, M., Rahman, S., Singh, A. K., Xanthidis, M., O’Kane, J. M., et al. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-50115-4_67 · Cited by 77
Shape-Former: Bridging CNN and Transformer via ShapeConv for multimodal image matching
Chen, J., Chen, X., Chen, S., Liu, Y., Rao, Y., Yang, Y., Wang, H., & Wu, D. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.030 · Cited by 77
An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping
Holmes, S. A., Klein, G., & Murray, D. W. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.189 · Cited by 77
Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning
Cao, X. S., Sun, C., & Yan, M. (2019) · IEEE Access · doi:10.1109/access.2019.2929120 · Cited by 76
Aerial 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
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
Multi-Camera Tracking and Mapping for Unmanned Aerial Vehicles in Unstructured Environments
Harmat, A., Trentini, M., & Sharf, I. (2014) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0085-y · Cited by 76
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
Charuco Board-Based Omnidirectional Camera Calibration Method
An, G. H., Lee, S., Seo, M., Yun, K., Cheong, W., & Kang, S. (2018) · Electronics · doi:10.3390/electronics7120421 · Cited by 76
State of the Art Report on Video‐Based Graphics and Video Visualization
Borgo, R., Chen, M., Daubney, B., Grundy, E., Heidemann, G., Höferlin, B., Höferlin, M., Leitte, H., Weiskopf, D., & Xie, X. (2012) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2012.03158.x · Cited by 77
Jiang, G., Yin, L., Jin, S., Tian, C., Ma, X., & Ou, Y. (2019) · Applied Sciences · doi:10.3390/app9102105 · Cited by 75
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
Chen, C., Lu, C. X., Wang, B., Trigoni, N., & Markham, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3112460 · Cited by 78
Yan, L., Hu, X., Zhao, L., Chen, Y., Wei, P., & Xie, H. (2022) · Remote Sensing · doi:10.3390/rs14030795 · Cited by 75
CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles
Reddy, N. D., Vo, M., & Narasimhan, S. G. (2018) · 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.2018.00204 · Cited by 79
A 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
Avoiding moving outliers in visual SLAM by tracking moving objects
Wangsiripitak, S., & Murray, D. W. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152290 · Cited by 74
Random forests versus Neural Networks — What's best for camera localization?
Massiceti, D., Krull, A., Brachmann, E., Rother, C., & Torr, P. H. S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989598 · Cited by 73
Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency
Zhao, Y., & Vela, P. (2020) · IEEE Transactions on robotics · doi:10.1109/tro.2020.2964138 · Cited by 82
On combining visual SLAM and visual odometry
Williams, B., & Reid, I. D. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509248 · Cited by 73
Self-citationMonocular Visual Odometry using a Planar Road Model to Solve Scale Ambiguity
Kitt, B., Rehder, J., Chambers, A. D., Schönbein, M., Lategahn, H., & Singh, S. (2018) · Figshare · doi:10.1184/r1/6555626 · Cited by 73
3D point cloud map based vehicle localization using stereo camera
Xu, Y., John, V., Mita, S., Tehrani, H., Ishimaru, K., & Nishino, S. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995765 · Cited by 73
Loop closure detection for visual SLAM systems using convolutional neural network
Zhang, X., Su, Y. L., & Zhu, X. (2017) · International Conference on Automation and Computing (ICAC) · doi:10.23919/iconac.2017.8082072 · Cited by 74
Comparison of ROS-based visual SLAM methods in homogeneous indoor environment
Ibragimov, I. Z., & Afanasyev, I. M. (2017) · Workshop on Positioning, Navigation and Communications (WPNC) · doi:10.1109/wpnc.2017.8250081 · Cited by 73
Motion capture from body-mounted cameras
Shiratori, T., Park, H. S., Sigal, L., Sheikh, Y. A., & Hodgins, J. K. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964926 · Cited by 73
Urban localization with camera and inertial measurement unit
Lategahn, H., Schreiber, M., Ziegler, J., & Stiller, C. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629552 · Cited by 74
EKF-Based Visual Inertial Navigation Using Sliding Window Nonlinear Optimization
Heo, S., Cha, J., & Park, C. G. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2866637 · Cited by 72
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
Chen, S., Wang, Z., & Prisacariu, V. A. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00125 · Cited by 72
Generic NDT mapping in dynamic environments and its application for lifelong SLAM
Einhorn, E., & Groß, H. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.08.008 · Cited by 72
EchoSLAM: Simultaneous localization and mapping with acoustic echoes
Kreković, M., Dokmanić, I., & Vetterli, M. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7471627 · Cited by 72
Simple yet stable bearing‐only navigation
Krajník, T., Faigl, J., Vonásek, V., Košnar, K., Kulich, M., & Přeučil, L. (2010) · Journal of Field Robotics · doi:10.1002/rob.20354 · Cited by 72
Marshall, J. A., Sun, W., & L’Afflitto, A. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.10.013 · Cited by 71
Lin, J., & Zhang, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3456473 · Cited by 71
Keypoint Signatures for Fast Learning and Recognition
Calonder, M., Lepetit, V., & Fua, P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_6 · Cited by 71
Combining monoSLAM with object recognition for scene augmentation using a wearable camera
Castle, R. O., Klein, G., & Murray, D. W. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2010.03.009 · Cited by 71
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
SalientDSO: Bringing Attention to Direct Sparse Odometry
Liang, H.-J., Sanket, N. J., Fermuller, C. M., & Aloimonos, Y. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2900980 · Cited by 71
Sensor Fusion for Augmented Reality
Hol, J. D., Schön, T. B., Gustafsson, F., & Slycke, P. J. (2006) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2006.301604 · Cited by 71
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
Zhang, J. Y., Ramanan, D., & Tulsiani, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_34 · Cited by 77
An 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
Probabilistic structure matching for visual SLAM with a multi-camera rig
Kaess, M., & Dellaert, F. (2009) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.07.006 · Cited by 70
BoRF: Loop-closure detection with scale invariant visual features
Zhang, H. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980273 · 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
Panoramic 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
FutureMapping: The Computational Structure of Spatial AI SystemsPreprint
Davison, A. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.11288 · Cited by 70
Self-citationAn Evaluation of Feature Matchers for Fundamental Matrix EstimationPreprint
Bian, J.-W., Wu, Y.-H., Zhao, J. Y., Liu, Y., Zhang, L., Cheng, M., & Reid, I. D. (2019) · arXiv (Cornell University) · doi:10.5244/c.33.89 · Cited by 70
Self-citationPseudo 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
Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope
Quan, M., Piao, S., Tan, M., & Huang, S.-S. (2019) · IEEE Access · doi:10.1109/access.2019.2930201 · Cited by 69
Localization Challenges for the Emergence of the Smart World
Pahlavan, K., Krishnamurthy, P. V., & Geng, Y. (2015) · IEEE Access · doi:10.1109/access.2015.2508648 · Cited by 68
Zhang, G., Vela, P. A., Karasev, P. A., & Brilakis, I. (2014) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12063 · Cited by 68
Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes
Vysotska, O., & Stachniss, C. (2015) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2512936 · Cited by 68
A critique of current developments in simultaneous localization and mapping
Huang, S., & Dissanayake, G. (2016) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881416669482 · Cited by 71
Hybrid Scene Compression for Visual Localization
Camposeco, F., Cohen, A., Pollefeys, M., & Sattler, 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.2019.00784 · Cited by 68
Metadata
- Authors
- Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O.
- Affiliations
- University of Oxford (GB)
- Centre National de la Recherche Scientifique (FR)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-06
- Volume
- 29
- Issue
- 6
- Pages
- 1052-1067
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2152671441
- Cited by
- 3,959
- References
- 57
- Citations by year
- 2026: 108 · 2025: 240 · 2024: 242 · 2023: 253 · 2022: 246 · 2021: 253 · 2020: 264 · 2019: 271 · 2018: 240 · 2017: 201 · 2016: 179 · 2015: 237 · 2014: 204 · 2013: 205 · 2012: 176
- FWCI
- 328.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (GR/R76790/02, GR/T24685/01, GR/R76790/01)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- monocular SLAM, real-time SLAM, monocular camera, 3D trajectory estimation, natural landmarks, probabilistic mapping, feature initialization, 3D localization, humanoid robots, augmented reality, handheld camera
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Systems, Image Enhancement, Image Interpretation, Computer-Assisted, Numerical Analysis, Computer-Assisted, Pattern Recognition, Automated, Photogrammetry, Signal Processing, Computer-Assisted, Video Recording, Information Storage and Retrieval, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
