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.
“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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Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070 · see citing works
Parallel Tracking and Mapping for Small AR Workspaces
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Bundle Adjustment — A Modern Synthesis
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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 · see citing works
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 · see citing works
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 · see citing works
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ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898 · see citing works
“Most modern stereo SLAM systems are keyframe based [7] and perform BA optimization in a local area to achieve scalability.”
BackgroundORB-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 · see citing works
Self-citationORB-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., & Tardós, J. D. (2020) · IEEE Transactions on robotics · doi:10.48550/arxiv.2007.11898 · Cited by 4,863
Self-citationPast, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596 · see citing works
Unmanned aerial systems for photogrammetry and remote sensing: A review
Colomina, I., & Molina, P. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.02.013 · Cited by 2,845 · see citing works
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885 · see citing works
“This comparison effort has been proposed in [83] and then extended in [84] for monocular approaches.”
Visual 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 · see citing works
“3 Comparison between MonoSLAM and PTAM The difference between the EKF based mapping in MonoSLAM and the BA-based mapping with keyframes in PTAM was discussed in the literature [34].”
MethodPL-SLAM: Real-time monocular visual SLAM with points and lines
Pumarola, A., Vakhitov, A., Agudo, A., Sanfeliu, A., & Moreno-Noguer, F. (2017) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2017.7989522 · Cited by 469 · see citing works
Geometry-Aware Learning of Maps for Camera LocalizationPreprint
Brahmbhatt, S., Gu, J., Kim, K., Hays, J., & Kautz, J. (2017) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1712.03342 · Cited by 458 · see citing works
“In visual SLAM, 3D landmarks (with feature descriptors of the corresponding image patches) are often defined as maps in both Bayesian filtering approaches [14, 41, 53] and key-frame based approaches [34, 42, 49].”
BackgroundA survey on vision-based UAV navigation
Lu, Y.-C., Xue, Z., Xia, G.-S., & Zhang, L.-P. (2018) · Geo-Spatial Information Science · doi:10.1080/10095020.2017.1420509 · Cited by 442 · see citing works
Visual SLAM and Structure from Motion in Dynamic Environments
Saputra, M. R. U., Markham, A., & Trigoni, N. (2018) · ACM Computing Surveys · doi:10.1145/3177853 · Cited by 413 · see citing works
CoSLAM: Collaborative Visual SLAM in Dynamic Environments
Zou, D., & Tan, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.104 · Cited by 378 · see citing works
A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping
Debeunne, C., & Vivet, D. (2020) · Italian National Conference on Sensors · doi:10.3390/s20072068 · Cited by 356 · see citing works
Robust monocular SLAM in dynamic environments
Tan, W., Liu, H.-M., Dong, Z., Zhang, G., & Bao, H. (2013) · International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2013.6671781 · Cited by 306 · see citing works
Schmuck, P., & Chli, M. (2018) · J. Field Robotics · doi:10.1002/rob.21854 · Cited by 264 · see citing works
“Strasdat, Montiel, and Davison (2012) conducted an investigation of filter ‐ and keyframe (KF) ‐ based SLAM and concluded that the latter is overall more promising for robust and practically applicable SLAM systems.”
BackgroundRGB-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 · see citing works
End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
Wang, S., Clark, R., Wen, H.-K., & Trigoni, N. (2018) · Int. J. Robotics Res. · doi:10.1177/0278364917734298 · Cited by 224 · see citing works
“constant velocity model (Davison et al., 2007; Mur– Artal et al., 2015; Strasdat et al., 2012; Williams and Reid, 2010), the covariance matrix is represented by six standard deviations of motion u as discussed in Section 3.”
BackgroundInfluentialS-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 · see citing works
“A decade later [31] showed that they are also less efficient –in terms of information processing per time unit– than a parallel tracking and mapping approach, the latest becoming the dominant algorithm to the current days.”
BackgroundSLAM; definition and evolution
Taheri, H., & Xia, Z.-C. (2021) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2020.104032 · Cited by 187 · see citing works
Unsupervised learning to detect loops using deep neural networks for visual SLAM system
Gao, X., & Tao, Z. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9516-2 · Cited by 179 · see citing works
“Many of the these systems are based on pose graph where the trajectory is built of a series of adjacent key-frames (Henry et al. 2012; Chen et al. 2007; Strasdat et al. 2012; Tian et al. 2013).”
BackgroundSemantics for Robotic Mapping, Perception and Interaction: A Survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Found. Trends Robotics · doi:10.1561/9781680837698 · Cited by 49 · see citing works
Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM
Mur-Artal, R., & Tardós, J. D. (2015) · Robotics: Science and Systems Conference · doi:10.15607/rss.2015.xi.041 · Cited by 140 · see citing works
“Modern feature-based techniques [8, 19, 12] are based on keyframes [20] and bundle adjustment (BA) optimization [23].”
MethodLianos, K., Schönberger, J. L., Pollefeys, M., & Sattler, T. (2018) · European Conference on Computer Vision · doi:10.1007/978-3-030-01225-0_15 · Cited by 129 · see citing works
“, using an extended Kalman Filter [33] or bundle adjustment [26,46].”
BackgroundMethodSurvey and evaluation of monocular visual-inertial SLAM algorithms for augmented reality
Li, J., Yang, B.-B., Chen, D., Wang, N., Zhang, G., & Bao, H. (2019) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2019.07.002 · Cited by 128 · see citing works
“As investigated, optimization-based methods can have superior accuracy over filtering-based ones [22] .”
MethodA review of monocular visual odometry
He, M., Zhu, C., Huang, Q., Ren, B., & Liu, J. (2019) · The Visual Computer · doi:10.1007/s00371-019-01714-6 · Cited by 127 · see citing works
GOMSF: Graph-Optimization Based Multi-Sensor Fusion for robust UAV Pose estimation
Mascaro, R., Teixeira, L., Hinzmann, T., Siegwart, R., & Chli, M. (2018) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2018.8460193 · Cited by 116 · see citing works
“This reinforces the findings of [5], that non-linear”
BackgroundResultRecent developments and applications of simultaneous localization and mapping in agriculture
Ding, H.-Z., Zhang, B.-H., Zhou, J., Yan, Y.-X., Tian, G.-Z., & Gu, B.-X. (2022) · J. Field Robotics · doi:10.1002/rob.22077 · Cited by 114 · see citing works
“method under the same calculation amount (Strasdat et al., 2012).”
MethodInfluentialImage-based camera localization: an overview
Wu, Y., Tang, F.-L., & Li, H. (2016) · Visual Computing for Industry, Biomedicine, and Art · doi:10.1186/s42492-018-0008-z · Cited by 114 · see citing works
Robust RGB-D Odometry Using Point and Line Features
Lu, Y., & Song, D. (2015) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2015.448 · Cited by 112 · see citing works
Lajoie, P.-Y., Ramtoula, B., Wu, F., & Beltrame, G. (2021) · Field Robotics · doi:10.55417/fr.2022032 · Cited by 105 · see citing works
“…filtering-based approaches were the most common among the early techniques (e.g., EKF (Rekleitis et al., 2003) and particle filters (Madhavan et al., 2004)), smoothing-based approaches quickly gained in popularity and are currently considered as superior in most applications (Strasdat et al., 2012).”
BackgroundTowards Real-Time Building Damage Mapping with Low-Cost UAV Solutions
Nex, F., Duarte, D., Steenbeek, A., & Kerle, N. (2019) · Remote Sensing · doi:10.3390/rs11030287 · Cited by 103 · see citing works
Edge Assisted Mobile Semantic Visual SLAM
Xu, J., Cao, H., Li, D., Huang, K., Qian, C., Shangguan, L., & Yang, Z. (2020) · IEEE INFOCOM 2020 - IEEE Conference on Computer Communications · doi:10.1109/infocom41043.2020.9155438 · Cited by 101 · see citing works
“Later, optimization-based methods [37] [38] came on stage and were turned out to be more accurate [39].”
MethodRobust Keyframe-based Monocular SLAM for Augmented Reality
Liu, H.-M., Zhang, G., & Bao, H. (2016) · International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar-adjunct.2016.0111 · Cited by 101 · see citing works
“As concluded in [31], keyframe-based BA outperforms filtering especially when N is large.”
BackgroundA review of visual inertial odometry from filtering and optimisation perspectives
Gui, J.-J., Gu, D.-B., Wang, S., & Hu, H.-S. (2015) · Adv. Robotics · doi:10.1080/01691864.2015.1057616 · Cited by 95 · see citing works
“The most popular criteria are the critical information contained in the data so that those keyframes are maintained during the entire processing.[19,34] Marginalising out states is equivalent to applying the Schur compliment to the least squared problem.”
MethodA Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM
Huang, G., Mourikis, A. I., & Roumeliotis, S. I. (2013) · IEEE Transactions on robotics · doi:10.1109/tro.2013.2267991 · Cited by 92 · see citing works
“Moreover, we introduce the OC-UKF, which attains better performance than the standard UKF, by ensuring that the observability requirements on the filter’s system model are satisfied.”
BackgroundMethodVisual navigation and docking for a planar type AUV docking and charging system
Wang, T., Zhao, Q.-C., & Yang, C.-J. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.108744 · Cited by 91 · see citing works
Online generative model personalization for hand tracking
Tkach, A., Tagliasacchi, A., Remelli, E., Pauly, M., & Fitzgibbon, A. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130830 · Cited by 91 · see citing works
“…and Cathey 1993; Bel-laire et al. 1995] that there are strong links between Levenberg-style algorithms and the Kalman filter, and that Kalman filters are useful to maintain uncertainty in visual tracking and SLAM [Strasdat et al. 2012], only recently have the advantages of both views been combined.”
Method3D Mapping and 6D Pose Computation for Real Time Augmented Reality on Cylindrical Objects
Tang, F.-L., Wu, Y., Hou, X., & Ling, H. (2020) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2019.2950449 · Cited by 90 · see citing works
Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles
Yang, S., Scherer, S., Yi, X., & Zell, A. (2017) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.03.018 · Cited by 88 · see citing works
“The advantages and disadvantages of filtering and keyframe-based methods are analyzed in the work of Strasdat et al. (2010a, 2012), suggesting that, in most modern applications, keyframe optimization gives the most accuracy per unit of computing time.”
MethodSequential Non-Rigid Structure from Motion Using Physical Priors
Agudo, A., Moreno-Noguer, F., Calvo, B., & Montiel, J. M. M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2469293 · Cited by 88 · see citing works
Self-citationAutonomous Quadrotor Flight Despite Rotor Failure With Onboard Vision Sensors: Frames vs. Events
Sun, S.-H., Cioffi, G., de Visser, C. D., & Scaramuzza, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3048875 · Cited by 85 · see citing works
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 · see citing works
Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments
Ferrera, M., Moras, J., Trouvé-Peloux, P., & Creuze, V. (2018) · Italian National Conference on Sensors · doi:10.3390/s19030687 · Cited by 83 · see citing works
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM SystemPreprint
Urban, S., & Hinz, S. (2016) · arXiv.org · doi:10.48550/arxiv.1610.07336 · Cited by 81 · see citing works
“But especially a large map and many observed features lead to accurate and robust SLAM pipelines (Strasdat et al., 2012).”
BackgroundMethodLiDAR, IMU, and camera fusion for simultaneous localization and mapping: a systematic review
Fan, Z., Zhang, L.-L., Wang, X.-Y., Shen, Y., & Deng, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11187-w · Cited by 80 · see citing works
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 78 · see citing works
“The SLAM problem is addressed under two broad paradigms of 1) Filtering based [11–15] and 1) optimization based approaches [16].”
BackgroundActive Structure From Motion: Application to Point, Sphere, and Cylinder
Spica, R., Giordano, P., & Chaumette, F. (2014) · IEEE Transactions on robotics · doi:10.1109/tro.2014.2365652 · Cited by 77
“A recent discussion about the pros/cons of both approaches in the context of Visual SLAM can be found in [1].”
BackgroundGLIM: 3D range-inertial localization and mapping with GPU-accelerated scan matching factors
Koide, K., Yokozuka, M., Oishi, S., & Banno, A. (2024) · Robotics Auton. Syst. · doi:10.1016/j.robot.2024.104750 · Cited by 75 · see citing works
“In the context of visual SLAM, it has been shown that the optimization-based approach outperforms the filtering-based approach in terms of accuracy because it re-linearizes past measurements [27], [28].”
MethodConsistent sparsification for graph optimization
Huang, G., Kaess, M., & Leonard, J. J. (2013) · 2013 European Conference on Mobile Robots · doi:10.1109/ecmr.2013.6698835 · Cited by 70 · see citing works
“Graph-based batch optimization methods for SLAM recently have prevailed (e.g., [1], [3], [4], [17]–[23]).”
MethodDifferentiable SLAM-net: Learning Particle SLAM for Visual Navigation
Karkus, P., Cai, S., & Hsu, D. (2021) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.2105.07593 · Cited by 70
“Optimization-based approaches apply bundle adjustment on a set of keyframes and local maps; and they are popular for both visual [53; 47; 46; 38] and lidar-based SLAM [28].”
BackgroundMethodMonocular localization in urban environments using road markings
Lu, Y., Huang, J.-W., Chen, Y.-T., & Heisele, B. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995762 · Cited by 61 · see citing works
“It is shown that bundle adjustment approaches can achieve higher accuracy due to the re-linearization when solving the non-linear optimization [30].”
BackgroundRobust RGB-D simultaneous localization and mapping using planar point features
Gao, X., & Tao, Z. (2015) · Robotics Auton. Syst. · doi:10.1016/j.robot.2015.03.007 · Cited by 54 · see citing works
Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey
Chghaf, M., Rodriguez, S., & Ouardi, A. E. (2022) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-022-01582-8 · Cited by 53 · see citing works
“Additionally, they are more memory-efficient and conventionally more accurate than filtering methods [116].”
BackgroundMarković, L., Kovac, M., Milijaš, R., Car, M., & Bogdan, S. (2021) · International Conference on Unmanned Aircraft Systems · doi:10.48550/arxiv.2109.04908 · Cited by 51
“The optimization methods [2], [3], are more precise, but often require a significant amount of computational resources.”
MethodGraph-Based Thermal–Inertial SLAM With Probabilistic Neural NetworksPreprint
Saputra, M. R. U., Lu, C. X., de Gusmão, P. P. B., Wang, B., Markham, A., & Trigoni, N. (2021) · IEEE Transactions on robotics · doi:10.48550/arxiv.2104.07196 · Cited by 51 · see citing works
“However, due to the nature of EKF algorithm which accumulates linearization errors across multiple frames, keyframe-based Bundle Adjustment (BA) approach is more widely used in the past decade since it has been shown to be more accurate than filtering [25].”
MethodXia, L.-L., Cui, J., Shen, R., Xu, X., Gao, Y., & Li, X. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420919185 · Cited by 51 · see citing works
Stereo Visual SLAM Based on Unscented Dual Quaternion Filtering
Bultmann, S., Li, K., & Hanebeck, U. (2019) · Fusion · doi:10.23919/fusion43075.2019.9011391 · Cited by 51 · see citing works
A Low Cost UWB Based Solution for Direct Georeferencing UAV Photogrammetry
Masiero, A., Fissore, F., & Vettore, A. (2017) · Remote Sensing · doi:10.3390/rs9050414 · Cited by 51
“Use of visual information for estimation of camera position and orientation has also been considered in the Simultaneous Localization and Mapping (SLAM) problem and, in particular, in visual SLAM [48].”
MethodWang, S., Chen, L., Gu, D.-B., & Hu, H.-S. (2014) · Robotics Auton. Syst. · doi:10.1016/j.robot.2014.05.004 · Cited by 51
Aslan, M. F., Durdu, A., Yusefi, A., & Yilmaz, A. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.09.001 · Cited by 50 · see citing works
Liu, W.-Z., Yu, B., Gan, Y.-M., Liu, Q., Tang, J., Liu, S.-S., & Zhu, Y. (2021) · Micro · doi:10.1145/3466752.3480077 · Cited by 50 · see citing works
“Comparing to the other popular class of SLAM algorithm based on non-linear filtering [27, 40, 41, 41, 74, 86], MAP is more robust in long-term localization and is more efficient, as quantified by accuracy per unit of computing time [72].”
BackgroundMethodOverview of Multi-Robot Collaborative SLAM from the Perspective of Data Fusion
Chen, W.-F., Wang, X., Gao, S.-P., Shang, G., Zhou, C., Li, Z., Xu, C., & Hu, K. (2023) · Machines · doi:10.3390/machines11060653 · Cited by 49 · see citing works
Shi, Y., Ji, S.-P., Shi, Z.-C., Duan, Y., & Shibasaki, R. (2012) · Italian National Conference on Sensors · doi:10.3390/s130100119 · Cited by 49 · see citing works
“BA is more accurate and theoretically rigorous than filtering because filter-SLAM marginalises the previous information, and BA-SLAM keeps the global optimum [11,17], proves that all of the corresponding rays intersect correctly, and avoids matching and model errors.”
BackgroundMethodUnderwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
Heshmat, M., Saoud, L. S., Abujabal, M., Sultan, A., Elmezain, M., Seneviratne, L. D., & Hussain, I. (2025) · Italian National Conference on Sensors · doi:10.3390/s25113258 · Cited by 48 · see citing works
ORB-SLAM Applied to Spacecraft Non-Cooperative Rendezvous
Dor, M., & Tsiotras, P. (2018) · AAS/AIAA Space Flight Mechanics Meeting · doi:10.2514/6.2018-1963 · Cited by 48 · see citing works
BIM-supported scan and flight planning for fully autonomous LiDAR-carrying UAVs
Song, C.-H., Chen, Z.-K., Wang, K., Luo, H., & Cheng, J. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104533 · Cited by 47 · see citing works
Align to locate: Registering photogrammetric point clouds to BIM for robust indoor localization
Chen, J.-J., Li, S., & Lu, W.-S. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108675 · Cited by 47 · see citing works
Visual SLAM: Why Bundle Adjust?
Parra, Á., Chin, T.-J., Eriksson, A. P., & Reid, I. (2019) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2019.8793749 · Cited by 47 · see citing works
Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception
Zhuang, L., Zhong, X., Xu, L., Tian, C., & Yu, W.-S. (2024) · Italian National Conference on Sensors · doi:10.3390/s24102980 · Cited by 46 · see citing works
Overview of deep learning application on visual SLAM
Li, S., Zhang, D., Xian, Y., Li, B.-J., Zhang, T., & Zhong, C. (2022) · Displays (Guildford) · doi:10.1016/j.displa.2022.102298 · Cited by 45 · see citing works
SLAM integrated mobile mapping system in complex urban environments
Li, S.-X., Li, G.-Y., Wang, L., & Qin, Y. (2020) · Isprs Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.05.012 · Cited by 44 · see citing works
Pair-Navi: Peer-to-Peer Indoor Navigation with Mobile Visual SLAM
Dong, E.-Q., Xu, J.-G., Wu, C., Liu, Y.-H., & Yang, Z. (2019) · IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737640 · Cited by 43 · see citing works
“Later, optimization-based methods [36] [13] came on stage and was demonstrated more accurate [37].”
MethodReview and classification of vision-based localisation techniques in unknown environments
Ben-Afia, A., Deambrogio, L., Salos, D., Escher, A.-C., Macabiau, C., Soulier, L., & Gay-Bellile, V. (2014) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2013.0389 · Cited by 43 · see citing works
Fang, X., Li, Q., Zhu, J., Chen, Z., Zhang, D., Wu, K., Ding, K., & Li, Q.-Q. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104494 · Cited by 42 · see citing works
A framework for maximum likelihood parameter identification applied on MAVs
Burri, M., Bloesch, M., Taylor, Z., Siegwart, R., & Nieto, J. I. (2018) · J. Field Robotics · doi:10.1002/rob.21729 · Cited by 42
Shepard, D. P., & Humphreys, T. (2014) · 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014 · doi:10.1109/plans.2014.6851506 · Cited by 41 · see citing works
“Thankfully, the highest accuracy for visual SLAM is obtained by employing a large number of image features, which results in a sparse structure that can be exploited by the batch estimator, and a smaller number of geographically-diverse images, as was shown in [14].”
BackgroundA Right Invariant Extended Kalman Filter for Object based SLAM
Song, Y., Zhang, Z., Wu, J., Wang, Y., Zhao, L., & Huang, S.-D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3139370 · Cited by 40 · see citing works
“However, assuming all the objects are observed at all the steps, the computational complexity of EKF methods is O(T ·N(3)) for the whole trajectory, while the computational complexity of pose graph optimization (using Schur complement) is O(T (3)+T 2 ·N), where T is the number of time steps and N is the number of obje…”
Information-Driven Path Planning
Bai, S., Shan, T., Chen, F., Liu, L.-T., & Englot, B. (2021) · Current Robotics Reports · doi:10.1007/s43154-021-00045-6 · Cited by 40 · see citing works
“accuracy; this process is called bundle adjustment [117].”
BackgroundPiao, J.-C., & Kim, S.-D. (2017) · Italian National Conference on Sensors · doi:10.3390/s17112567 · Cited by 40 · see citing works
“This method is superior to the traditional filter-based method [6] and is currently the most commonly used visual SLAM method.”
MethodDRM-SLAM: Towards dense reconstruction of monocular SLAM with scene depth fusion
Ye, X.-C., Ji, X., Sun, B.-L., Chen, S.-L., Wang, Z., & Li, H.-J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.044 · Cited by 39 · see citing works
Pseudo-3D Vision-Inertia Based Underwater Self-Localization for AUVs
Wang, Y., Ma, X.-R., Wang, J., & Wang, H.-Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2993715 · Cited by 39 · see citing works
Pose fusion with chain pose graphs for automated driving
Merfels, C., & Stachniss, C. (2016) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros.2016.7759482 · Cited by 39 · see citing works
Wang, X.-B., Li, X.-X., Shen, Z., Li, X., Zhou, Y., & Chang, H. (2023) · GPS Solutions · doi:10.1007/s10291-023-01538-x · Cited by 38
“Based on the aforementioned properties, the FGO-based estimator can resist the outliers effectively and achieve smoother results in comparison with the conventional filter-based methods (Wen et al. 2020; Strasdat et al. 2012).”
ResultAttention-SLAM: A Visual Monocular SLAM Learning From Human Gaze
Li, J., Pei, L., Zou, D., Xia, S., Wu, Q., Li, T., Sun, Z., & Yu, W.-X. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3038432 · Cited by 38 · see citing works
“We compute the entropy reduction γ [53], [54] between ORB-SLAM and Attention-SLAM.”
MethodDifferentiable Factor Graph Optimization for Learning Smoothers
Yi, B., Lee, M. A., Kloss, A., Martín-Martín, R., & Bohg, J. (2021) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros51168.2021.9636300 · Cited by 37 · see citing works
“In contrast, smoothing-based inference can be robustly applied to large-scale systems because computations are inherently sparse, allowing efficient optimization via either sparse matrix factorization [12] or preconditioned conjugate gradient methods [21], and a runtime that grows linearly with state dimensionality [1…”
InfluentialReview of Visual Simultaneous Localization and Mapping Based on Deep Learning
Zhang, Y., Wu, Y., Tong, K., Chen, H., & Yuan, Y.-B. (2023) · Remote Sensing · doi:10.3390/rs15112740 · Cited by 36 · see citing works
A Stable and Accurate Marker-Less Augmented Reality Registration Method
Gao, Q.-H., Wan, T.-R., Tang, W., & Chen, L. (2017) · International Conference on Cyberworlds · doi:10.1109/cw.2017.44 · Cited by 35 · see citing works
“Initially the Simultaneous Localization and Mapping (SLAM) algorithm has been used mainly in robotics for positioning robots in unknown environments [4] [5].”
MethodLoop closure detection for visual SLAM using PCANet features
Xia, Y., Li, J., Qi, L., & Fan, H. (2016) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn.2016.7727481 · Cited by 34 · see citing works
Continuous-Time Estimation of Attitude Using B-Splines on Lie Groups
Sommer, H., Forbes, J., Siegwart, R., & Furgale, P. (2016) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g001149 · Cited by 34 · see citing works
Real-Time Monocular SLAM With Low Memory Requirements
Bresson, G., Féraud, T., Aufrère, R., Checchin, P., & Chapuis, R. (2015) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2014.2376780 · Cited by 34 · see citing works
“, a comparison of monocular SLAM algorithms [27] has shown that, with important resources, BAs seem more accurate.”
BackgroundMotion segmentation based robust RGB-D SLAM
Wang, Y.-B., & Huang, S.-D. (2014) · Proceeding of the 11th World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2014.7053228 · Cited by 34 · see citing works
“In addition to suffering from the inconsistency problem [7], filters have also been shown in [58] to produce less accurate results than keyframe-based BA.”
ResultRobust Selective Stereo SLAM without Loop Closure and Bundle Adjustment
Bellavia, F., Fanfani, M., Pazzaglia, F., & Colombo, C. (2013) · International Conference on Image Analysis and Processing · doi:10.1007/978-3-642-41181-6_47 · Cited by 34 · see citing works
Improved Preintegration Method for GNSS/IMU/In-Vehicle Sensors Navigation Using Graph Optimization
Bai, S.-Y., Lai, J., Lyu, P., Cen, Y., & Ji, B. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3115619 · Cited by 33 · see citing works
“Recently, the graph optimization-based method has been popular in the robotics field [9], [10].”
MethodA survey: which features are required for dynamic visual simultaneous localization and mapping?
Xu, Z., Rong, Z., & Wu, Y. (2021) · Visual Computing for Industry, Biomedicine, and Art · doi:10.1186/s42492-021-00086-w · Cited by 33 · see citing works
Wen, S.-H., Zhao, Y., Liu, X., Sun, F., Lu, H., & Wang, Z. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3041852 · Cited by 33 · see citing works
“In contrast, keyframe-based VSLAM method can optimize current pose only using a small set of past frames and shows higher accuracy with less computation than a filter-based frameworks [33].”
MethodPose Interpolation for Laser‐based Visual Odometry
Tong, C.-H., Anderson, S. R., Dong, H., & Barfoot, T. (2014) · J. Field Robotics · doi:10.1002/rob.21537 · Cited by 32 · see citing works
INCREMENTAL REAL-TIME BUNDLE ADJUSTMENT FOR MULTI-CAMERA SYSTEMS WITH POINTS AT INFINITY
Schneider, J., Läbe, T., & Förstner, W. (2013) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-1-w2-355-2013 · Cited by 32 · see citing works
Advancements in perception system with multi-sensor fusion for embodied agents
Du, H., Ren, L., Wang, Y., Cao, X. S., & Sun, C. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102859 · Cited by 27 · see citing works
Model order reduction for real-time data assimilation through Extended Kalman Filters
González, D., Badías, A., Alfaro, I., Chinesta, F., & Cueto, E. (2017) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2017.08.041 · Cited by 31 · see citing works
“To this end, among different possibilities, we can cite Simultaneous Localization and Mapping (SLAM) techniques [2] [38].”
MethodPöppl, F., Neuner, H., Mandlburger, G., & Pfeifer, N. (2023) · Isprs Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.022 · Cited by 30 · see citing works
- 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 › Engineering › Aerospace Engineering › 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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