The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Visual SLAM: Why filter?
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“MAP estimation has been proven to be more accurate and efficient than original approaches for SLAM based on nonlinear filtering.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [236]
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Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
MonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959
Self-citationiSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362
Adaptive relative bundle adjustment
Sibley, G., Mei, C., Reid, I., & Newman, P. (2009) · Robotics: Science and Systems V · doi:10.15607/rss.2009.v.023 · Cited by 139
Flagged first, then by citations
Research works printed but not linked: 15
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- Bell, 1993, The iterated Kalman filter update as a Gauss–Newton method, IEEE Trans. Autom. Control., 38, 294, 10.1109/9.250476 · DOI
- Chiuso, 2002, Structure from motion causally integrated over time, IEEE Trans. Pattern Anal. Mach. Intell., 24, 523, 10.1109/34.993559 · DOI
- Chli, 2009, Active matching for visual tracking, Robot. Auton. Syst., 57, 1173, 10.1016/j.robot.2009.07.010 · DOI
- Civera, 2008, Inverse depth parametrization for monocular SLAM, IEEE Trans. Rob., 24, 932, 10.1109/TRO.2008.2003276 · DOI
- Civera, 2010, 1-point RANSAC for EKF filtering. Application to real-time structure from motion and visual odometry, J. Field Rob., 27, 609, 10.1002/rob.20345 · DOI
- Dyer, 1969, Extension of square-root filtering to include process noise, J. Optim. Theory Appl., 3, 444, 10.1007/BF00929358 · DOI
- E. Eade. Monocular Simultaneous Localisation and Mapping. PhD thesis, University of Cambridge, 2008.
- Kaess, 2008, iSAM: incremental smoothing and mapping, IEEE Trans. Rob., 24, 1365, 10.1109/TRO.2008.2006706 · DOI · Paper page
- Konolige, 2008, FrameSLAM: from bundle adjustment to real-time visual mapping, IEEE Trans. Rob., 24, 1066, 10.1109/TRO.2008.2004832 · DOI
- Mahony, 2002, The geometry of the Newton method on non-compact Lie groups, J. Glob. Optim., 23, 309, 10.1023/A:1016586831090 · DOI
- Mei, 2011, RSLAM: a system for large-scale mapping in constant-time using stereo, Int. J. Comput. Vision, 94, 198, 10.1007/s11263-010-0361-7 · DOI
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Cited by
Sorted by citations
Items 401–426 of 426
An Extended Stochastic Cloning Method for Fusing Multi-relative Measurements
Zhang, H., Baumbach, D., Grießbach, D., & Börner, A. (2019) · ACPR Workshops · doi:10.1007/978-981-15-3651-9_23
Обзор современных методов визуальной одометрии
Терехов, М. А. (2019) · doi:10.32603/2071-2340-2019-3-5-14
Robot Indoor Text Contents Recognition Based on Visual SLAM
Hu, L. (2019) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1302/3/032004
Collaborative SLAM for outdoor environments
Samamé, L. F. C. (2019) · doi:10.70675/8357af92z708bz49a4zb4a3z2b74fa1b83cd
Evaluation of Lightweight Local Descriptors for Level Ground Navigation with Monocular SLAM
Chen, W.-Y., Wan, Y., Ou, S.-Q., & Xue, Z.-D. (2018) · Chinese Conference on Pattern Recognition and Computer Vision · doi:10.1007/978-3-030-03341-5_29
Design and evaluation of visual SLAM method based on Realsense for mobile robots
Yan, Y., Gai, S., Li, C., Zhou, F.-Y., Fan, Y., & Liu, P. (2018) · ACM Cloud and Autonomic Computing Conference · doi:10.1109/cac.2018.8623145
“The positioning and map construction processes have a mutual and interdependent relationship [7-9].”
BackgroundReal-Time Generative Hand Modeling and Tracking
Tkach, A. (2018) · doi:10.5075/epfl-thesis-8573
Edge and Intensity based Visual Odometry for RGB-D Camera
Yao, E.-L., Zhang, H., Zhang, G.-L., & Xu, H. (2018) · IEEE Chinese Guidance, Navigation and Control Conference · doi:10.1109/gncc42960.2018.9019066
“As mentioned in [4], optimization based methods of visual odometry are better than the filter based methods.”
MethodResearch on Unmanned Vehicle Positioning Technology Based on Multi-sensor Fusion
Xu, F., Wang, H., & Fang, B. (2018) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/pcmm2018/23656
“Simultaneous localization and mapping (SLAM) [1,2] refers to the process in which the robot carries the corresponding sensor to model the surrounding environment during the exploration of the unknown region, and meanwhile the estimation of its own motion [3,4].”
BackgroundA combined system of redundant MEMS and laser radar
Wang, Z.-Q., Li, D., Lei, Y., & Zhang, Y.-S. (2017) · Cybersecurity and Cyberforensics Conference · doi:10.23919/chicc.2017.8028311
“Under these kinds of circumstances, constraints between those coincided positions are often not used [13].”
BackgroundAn accurate and efficient monocular mixed match SLAM
Dai, W.-C., Zhang, Y., & Li, P. (2017) · Cybersecurity and Cyberforensics Conference · doi:10.23919/chicc.2017.8028440
Global-Local Airborne Mapping (GLAM): Reconstructing a City from Aerial VideosPreprint
Vohra, H., Bazik, M., Antone, M. E., Mundy, J., & Stephenson, W. (2017) · arXiv.org · doi:10.48550/arxiv.1706.01580
“Monocular SLAM is a long-researched subject that has seen evolution from filter based [7, 31] to keyframe based [26, 30] approaches.”
BackgroundVision-Based Navigation and Parameter Identification for Unmanned Aerial Vehicles
Burri, M. (2017) · doi:10.3929/ethz-b-000169095
“A good comparison with further detail can for instance be found in [123].”
BackgroundThe Application of Monocular Vision Camera Calibration and Feature Matching in SLAM
Wangpeng, Zhaohanqing, Wangjiangfeng, Liuhongbin, & Chenwei (2017) · AIACT '17 · doi:10.1145/3080845.3080876
“A typical map representation has a metric map, which emphasizes precisely the positional relationship of the objects in the map[16].”
BackgroundScene Reconstruction And Understanding By RGB-D Sensors
Fioraio, N. (2015) · doi:10.6092/unibo/amsdottorato/6941
“Moreover, [88] showed that there exist particular situations where Gaussian filters perform constantly worse than bundle adjustment, therefore in this thesis we will always use a BA-style formulation for camera path estimation.”
BackgroundMethodInfluentialCamera oscillation pattern for VSLAM: Translational versus rotational
Heshmat, M., Abdellatif, M., Nakamura, K., Abouelsoud, A., & Babaguchi, N. (2014) · International Conference on 3D Imaging · doi:10.1109/ic3d.2014.7032598
“enough physical clues to get the landmarks distances, especially, in the forward and curved robot motion [15].”
Localisation d'un véhicule à l'aide d'un SLAM visuel contraint
Larnaout, D. (2014) · doi:10.70675/b4de7ebaz4a60z4851z93bezabff7116de25
“On compare alors le SLAM contraint en question avec notre méthode de segmentation et celle utilisée par Tamaazousti et al. (2011) et Lothe et al.”
Hanada, S., Tanaka, K., & Inagaki, Y. (2013) · IEEE International Conference on Robotics and Biomimetics · doi:10.1109/robio.2013.6739758
“Consider a robot vision system builds a map of its local surroundings, via SLAM [4] or SfM [5], and compares the local map to a collection of N global maps”
BackgroundSP-VO: RGB-D Visual Odometry Using Static Parts Toward Dynamic Environments
Jeon, H., & Oh, J. (2023) · IEEE Access · doi:10.1109/access.2023.3275739
“relation has also been reported in previous research [40] on static environments.”
ResultUsing Video Motion Vectors for Structure from Motion 3D Reconstruction
Turner, R. N. C., Banerjee, N., & Banerjee, S. (2022) · Signal Processing and Multimedia Applications · doi:10.5220/0011263600003289
“In addition, keyframe selection on a sliding window for local BA could be implemented (Strasdat et al., 2012).”
Method3OFRR-SLAM: Visual SLAM with 3D-Assisting Optical Flow and Refined-RANSAC
Zhai, Y., Tang, F.-L., & Wu, Y. (2021) · Asian Conference on Pattern Recognition · doi:10.1007/978-3-031-02375-0_11
“It is proved in [21] that the keyframe-based visual SLAM outperforms filterbased visual SLAM.”
MethodRobust Monocular Visual-Inertial SLAM Using Nonlinear Optimization
Duo, J.-Y., Ji, L., & Zhao, L. (2020) · doi:10.1007/978-981-15-8450-3_59
Video Frames Selection Method for 3D Reconstruction Depending on ROS-Based Monocular SLAM
Yousif, Y., & Hatem, I. (2020) · doi:10.1007/978-3-030-45956-7_11
Jeon, J.-S., Kim, H.-J., & Shim, D. (2019) · doi:10.5370/kiee.2019.68.1.182
“SLAM의 필터링 기법보다는 SfM의 최적화 기법이 성 능이 좋다는 결과가 발표되기도 했다[1].”
Strategies and Methods for Using Augmented Reality
Piechaczek, D., & Jóźwiak, I. (2019) · doi:10.29119/1641-3466.2018.134.15
Variational approaches for motion and structure from monocular video
Neufeld, A. (2017) · doi:10.11588/heidok.00023722
“An application to monocular SLAM is presented in [81], where each frame is mapped into the same reference view.”
Method
Metadata
- Authors
- Strasdat, H., Montiel, J. M. M., & Davison, A. J.
- Affiliations
- Imperial College London (GB)
- Universidad de Zaragoza (ES)
- Corresponding author
- Strasdat, Hauke
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 30
- Issue
- 2
- Pages
- 65-77
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W49679257
- Cited by
- 439
- References
- 0
- Citations by year
- 2026: 9 · 2025: 25 · 2024: 28 · 2023: 37 · 2022: 44 · 2021: 47 · 2020: 47 · 2019: 50 · 2018: 33 · 2017: 41 · 2016: 15 · 2015: 24 · 2014: 19 · 2013: 17 · 2012: 2
- FWCI
- 33.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Image and Vision Computing (publishedVersion)
- Funders
- Imperial College London
- Ministerio de Economía y Competitividad
- Universidad de Zaragoza
- FP7 Information and Communication Technologies
- 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
- visual SLAM, structure from motion, filtering algorithms, sparse bundle adjustment, real-time SLAM, stereo SLAM, monocular SLAM, entropy reduction, computational cost, Monte Carlo simulation
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