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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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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
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Sorted by citations
Items 101–200 of 426
Visual SLAM With Drift-Free Rotation Estimation in Manhattan World
Liu, J., & Meng, Z.-Y. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3014648 · Cited by 30
“It is also shown that the optimization-based method is more suitable for V-SLAM application due to its robustness and superior accuracy [1].”
MethodInformation-Driven Direct RGB-D Odometry
Fontan, A., Civera, J., & Triebel, R. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00498 · Cited by 30
“that is, how much more accuracy is obtained by measuring Y instead of X [23].”
BackgroundMethodLi, X., Wang, Y., & Khoshelham, K. (2019) · Acta Geodaetica et Geophysica · doi:10.1007/s40328-019-00254-8 · Cited by 30
GIVE: A Tightly Coupled RTK-Inertial–Visual State Estimator for Robust and Precise Positioning
Wang, X.-B., Li, X.-X., Chang, H., Li, S., Shen, Z., & Zhou, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3282296 · Cited by 30
“Restrictions apply. approaches [8], [9], [10].”
A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering
Chen, H., Wu, W., Zhang, S., Wu, C., & Zhong, R. (2023) · Remote Sensing · doi:10.3390/rs15030790 · Cited by 34
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames
Lee, J., Back, M., Hwang, S., & Chun, I. (2021) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2022.3228525 · Cited by 29
“The keyframe-based SLAM improved the localization accuracy and computational efficiency of filter-based methods [9], and became the de facto standard in monocular SLAM [5].”
MethodSquare Root Marginalization for Sliding-Window Bundle Adjustment
Demmel, N., Schubert, D., Sommer, C., Cremers, D., & Usenko, V. (2021) · IEEE International Conference on Computer Vision · doi:10.1109/iccv48922.2021.01301 · Cited by 28
“Square root filters are able to improve numerical stability and let the system run on single-precision hardware [16, 1, 5, 29] and in particular information filters are closely related to optimizationbased approaches [26].”
Accurate Monocular Visual-Inertial SLAM Using a Map-Assisted EKF Approach
Quan, M.-X., Piao, S.-H., Tan, M., & Huang, S.-S. (2017) · IEEE Access · doi:10.1109/access.2019.2904512 · Cited by 28
“In [1], it was shown that nonlinear optimization-based approaches provide better accuracy than filtering approaches by its capability to relinearize the state at each iteration, therefore avoiding integrated error from linearization, however it leads to higher computational demands.”
BackgroundMethodZhang, G., Kontitsis, M., Filipe, N., Tsiotras, P., & Vela, P. A. (2015) · Journal of Field Robotics · doi:10.1002/rob.21575 · Cited by 24
Robustness to lighting variations: An RGB-D indoor visual odometry using line segments
Lu, Y., & Song, D. (2015) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros.2015.7353447 · Cited by 28
Bundle Adjustment for Multi-Camera Systems with Points at Infinity
Schneider, J., Schindler, F., Läbe, T., & Förstner, W. (2012) · doi:10.5194/isprsannals-i-3-75-2012 · Cited by 27
“Multi-camera systems are regularly used for odometry, especially stereo camera systems, e.g. (Mostafa and Schwarz, 2001; Strasdat et al., 2012) and more than two cameras e.g. in (Maas, 1999) or (Kaess and Dellaert, 2006).”
BackgroundFusion of binocular vision, 2D lidar and IMU for outdoor localization and indoor planar mapping
Liu, Z., Li, Z., Liu, A., Sun, Y., & Jing, S. (2022) · Measurement science and technology · doi:10.1088/1361-6501/ac9ed0 · Cited by 26
Constructive Observer Design for Visual Simultaneous Localisation and Mapping
van Goor, P., Mahony, R., Hamel, T., & Trumpf, J. (2020) · at - Automatisierungstechnik · doi:10.1016/j.automatica.2021.109803 · Cited by 26
Observers for Position Estimation Using Bearing and Biased Velocity Information
Bras, F. L., Hamel, T., Mahony, R., & Samson, C. (2017) · doi:10.1007/978-3-319-55372-6_1 · Cited by 26
A flexible framework for accurate LiDAR odometry, map manipulation, and localization
Blanco, J. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251316881 · Cited by 20
“These techniques have an obvious direct application to optimization-based (Strasdat et al. 2012) odometry frameworks that follow the frame-to-frame paradigm.”
Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms
Eyvazpour, R., Shoaran, M., & Karimian, G. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10310-5 · Cited by 25
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
Bürki, M., Schaupp, L., Dymczyk, M., Dubé, R., Cadena, C., Siegwart, R., & Nieto, J. I. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8814017 · Cited by 25
Wilbers, D., Merfels, C., & Stachniss, C. (2019) · International Conference on Robotic Computing · doi:10.1109/irc.2019.00040 · Cited by 25
Accurate keyframe selection and keypoint tracking for robust visual odometry
Fanfani, M., Bellavia, F., & Colombo, C. (2016) · Machine Vision and Applications · doi:10.1007/s00138-016-0793-3 · Cited by 25
Robust and accurate monocular visual navigation combining IMU for a quadrotor
Zheng, W., Zhou, F., & Wang, Z.-F. (2015) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2015.7032904 · Cited by 25
Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model
Nilsson, J., Fredriksson, J., & Odblom, A. C. E. (2014) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2014.2322196 · Cited by 25
GNSS‐stereo‐inertial SLAM for arable farming
Cremona, J., Civera, J., Kofman, E., & Pire, T. (2023) · J. Field Robotics · doi:10.1002/rob.22232 · Cited by 23
“This is however suboptimal compared to tightly-coupled methods (Strasdat et al., 2012), which model the correlations between state variables and sensor measurements.”
BackgroundA Multi-spectral Dataset for Evaluating Motion Estimation Systems
Dai, W.-C., Zhang, Y., Chen, S., Sun, D., & Kong, D. (2020) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra48506.2021.9561906 · Cited by 23
“These motion estimation methods can be categorized into filter-based [11] and factor-graphoptimization-based methods [12].”
MethodObject registration in semi-cluttered and partial-occluded scenes for augmented reality
Gao, Q.-H., Wan, T.-R., Tang, W., & Chen, L. (2018) · Multimedia tools and applications · doi:10.1007/s11042-018-6905-5 · Cited by 23
“Initially, SLAM algorithms are used mainly in robotics for robots navigation in unknown environments [4, 45], but more recently, researchers start to utilize the state of the art SLAM algorithms for virtual information and virtual object registrations in AR.”
MethodConsistent unscented incremental smoothing for multi-robot cooperative target tracking
Huang, G., Kaess, M., & Leonard, J. (2014) · Robotics Auton. Syst. · doi:10.1016/j.robot.2014.08.007 · Cited by 23
EVALUATION OF SIFT AND SURF FOR VISION BASED LOCALIZATION
Qu, X., Soheilian, B., Habets, E., & Paparoditis, N. (2016) · 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/isprs-archives-xli-b3-685-2016 · Cited by 22
“It is well known that the bundle adjustment is more precise than SLAM (Strasdat et al., 2012), but it could become time consuming with the increasing quantity of images because of its high complexity.”
BackgroundFactor graph-based PPP-RTK for accurate and robust positioning in urban environments
Li, X., Li, X.-X., Wang, X.-B., Chang, H., Tan, Y., & Shen, Z. (2024) · Journal of Geodesy · doi:10.1007/s00190-024-01828-3 · Cited by 21
“As a result, factor graph optimization has the potential to theoretically obtain more accurate and robust solutions over the traditional Kalman filter (Strasdat et al. 2012; Watson and Gross 2017; Wen et al. 2020).”
Fast Semantic-Aware Motion State Detection for Visual SLAM in Dynamic Environment
Singh, G., Wu, M.-Q., Đỗ, M. V., & Lam, S.-K. (2022) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2022.3213694 · Cited by 21
“Most recent vSLAM systems are keyframe-based [5], [41] and use keyframes for pose and map refinement by bundle adjustment optimization.”
MethodImproved IMU Preintegration with Gravity Change and Earth Rotation for Optimization-Based GNSS/VINS
Jiang, J., Niu, X., & Liu, J.-N. (2020) · Remote Sensing · doi:10.3390/rs12183048 · Cited by 21
“For achieving a real-time performance, the number of mappoints in the state of the filter is strictly limited, which also decreases the accuracy of information fusion [22].”
BackgroundWhere am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey
Dhiman, N. K., Deodhare, D., & Khemani, D. (2015) · Sadhana · doi:10.1007/s12046-015-0402-6 · Cited by 20
LineSLAM: Visual Real Time Localization Using Lines and UKF
Perdices, E., López, L., & Cañas, J. (2013) · ROBOT · doi:10.1007/978-3-319-03413-3_49 · Cited by 21
“In [10] both methods MonoSLAM and PTAM were compared and they came to the conclusion that EKF based approaches work better for small feature sets, whereas PTAM based approaches are more suitable elsewhere.”
MethodChen, W., Chen, S., Leng, J.-W., Wang, J.-K., Guan, Y.-S., Meng, M. Q.-H., & Zhang, H. (2024) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2024.3438165 · Cited by 20
“On the other hand, the back-end utilizes tools from optimization, probability theory [16], and graph theory [17] to generate the final state estimates using the outputs from the front-end, including local optimization, global optimization, and so forth.”
MethodJohnson, J. C., Mangelson, J. G., Barfoot, T., & Beard, R. (2024) · arXiv.org · doi:10.48550/arxiv.2402.00399 · Cited by 20
“Batch estimation has been shown to offer better performance than traditional filtering [9], in large part because the nonlinearities in the measurement models are iteratively relinearized about the current best guess rather than linearizing only once.”
BackgroundCollaborative Augmented Reality on Smartphones via Life-long City-scale Maps
Platinsky, L., Szabados, M., Hlasek, F., Hemsley, R., Pero, L. D., Pancik, A., Baum, B., Grimmett, H., & Ondruska, P. (2020) · International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar50242.2020.00081 · Cited by 20
Three-dimensional robot localization using cameras in wireless multimedia sensor networks
Feng, S., Shen, S., Huang, L., Champion, A. C., Yu, S., Wu, C.-D., & Zhang, Y.-Z. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102425 · Cited by 20
An experimental study on feature-based SLAM for multi-legged robots with RGB-D sensors
Nowicki, M. R., Belter, D., Kostusiak, A., Čížek, P., Faigl, J., & Skrzypczyński, P. (2017) · Industrial robot · doi:10.1108/ir-11-2016-0340 · Cited by 20
Relating Random Vector and Random Finite Set Estimation in Navigation, Mapping, and Tracking
Leung, K. Y. K., Inostroza, F., & Adams, M. D. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2701330 · Cited by 15
Application of Augmented Reality and Robotic Technology in Broadcasting: A Survey
Yan, D., & Hu, H.-S. (2017) · Robotics · doi:10.3390/robotics6030018 · Cited by 19
“Compare these two types of SLAM, key-frame-based approach outperform filter-based SLAM in both modeling and tracking performance, especially in small workplace [70,71].”
MethodAn Automatic Key-Frame Selection Method for Monocular Visual Odometry of Ground Vehicle
Lin, X., Wang, F.-H., Guo, L., & Zhang, W.-W. (2019) · IEEE Access · doi:10.1109/access.2019.2916901 · Cited by 19
“[20] shows that key-frame based method is better than filter-based method when there are more landmarks in 2010.”
BackgroundMethodChen, M., Ubul, K., Xu, X.-B., Aysa, A., & Muhammat, M. (2022) · Italian National Conference on Sensors · doi:10.3390/s22051899 · Cited by 18
“The keyframe-based technique is more accurate than the filter-based method at the same computational cost [18].”
MethodReal-Time Robust Stereo Visual SLAM System Based on Bionic Eyes
Liu, Y., Zhu, D.-C., Peng, J., Wang, X., Wang, L., Chen, L., Li, J.-M., & Zhang, X.-L. (2020) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2020.3011981 · Cited by 18
“We formulate the local bundle adjustment [34] problem by points reprojection error.”
MethodBIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal Plane
Murai, R., Sajad Saeedi, G., & Kelly, P. H. J. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341151 · Cited by 18
Lourenço, P., Batista, P., Oliveira, P., & Silvestre, C. (2018) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.11.001 · Cited by 18
Monocular visual odometry: Sparse joint optimisation or dense alternation?
Platinsky, L., Davison, A., & Leutenegger, S. (2017) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2017.7989599 · Cited by 18
Self-citationSensor Fusion for Self-Localisation of Automated Vehicles
Merfels, C., & Stachniss, C. (2017) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-017-0008-1 · Cited by 18
“STRASDAT et al. (2012) show that mainly because of that reason filtering performs suboptimal even for short time frames when compared to smoothing.”
BackgroundMoreno, F.-A., Blanco, J., & González-Jiménez, J. (2016) · Int. J. Robotics Res. · doi:10.1177/0278364915619238 · Cited by 18
“A thorough comparison between filtering and BA solutions is presented in the works (Strasdat et al. 2010) and (Strasdat et al. 2012), ultimately advocating for the application of BA as it outperforms filtering methods in terms of accuracy per unit of computation time.”
MethodData-Efficient Collaborative Decentralized Thermal-Inertial Odometry
Polizzi, V., Hewitt, R. A., Hidalgo-Carrió, J., Delaune, J., & Scaramuzza, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3194675 · Cited by 17
“filter-based approaches for structure from motion has been rigorously analyzed in [17].”
BackgroundAasim, W. F. A. W., Okasha, M., & Faris, W. F. (2022) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-022-01643-y · Cited by 17
“Considered as a milestone and the first of such approaches to utilise BA and multi-threading; PTAM [60] demonstrated significant advantage over filtering methods in terms of accuracy as it is able to handle a larger number of feature points [61].”
A SLAM System Based on RGBD Image and Point-Line Feature
Li, D., Liu, S., Xiang, W., Tan, Q., Yuan, K., Zhang, Z., & Hu, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3049467 · Cited by 20
“Strasdat pointed out that the result of graph optimization is better than that of the filter-based method [17].”
MethodOn the Redundancy Detection in Keyframe-Based SLAM
Schmuck, P., & Chli, M. (2019) · International Conference on 3D Vision · doi:10.1109/3dv.2019.00071 · Cited by 17
“Keyframebased systems choose a subset of the most representative frames from all incoming frames to be stored in the local map, the keyframes (KFs), and are now well-established in the SLAM literature [19, 22, 25], gaining substantial ground over filtering-based techniques [31].”
BackgroundRobust Keyframe-Based Monocular SLAM for Augmented Reality
Liu, H.-M., Zhang, G.-F., & Bao, H. (2016) · 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) · doi:10.1109/ismar.2016.24 · Cited by 17
Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes
Ji, S.-P., Shi, Y., Jie, S., Shao, X.-W., Shi, Z.-C., Yuan, X., Yang, P., Wu, W.-B., Tang, H., & Shibasaki, R. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.03.005 · Cited by 17
Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques
Malagon-Soldara, S. M., Toledano-Ayala, M., Soto-Zarazúa, G. M., Carrillo-Serrano, R., & Rivas-Araiza, E. (2015) · IEEE International Conference on Robotics and Automation · doi:10.11591/ijra.v4i1.968 · Cited by 17
“According to [22] two methodologies have been prevalent in live motion and structure estimation from a single moving video camera: i) filtering approaches that fuse measurements from all images sequentially by updating probability distributions over features and camera parameters; and ii) bundle…”
MethodLearning Type-2 Fuzzy Logic for Factor Graph Based-Robust Pose Estimation With Multi-Sensor Fusion
Dinh, N. V., & Kim, G.-W. (2023) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2023.3234595 · Cited by 16
Towards Robust Visual-Inertial Odometry with Multiple Non-Overlapping Monocular Cameras
He, Y., Yu, H., Yang, W., & Scherer, S. (2022) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros47612.2022.9981664 · Cited by 16
“ROVIO has the least CPU usages (around 60%), but the backend EKF is less efficient compared to the bundle adjustment method [14].”
Method3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
Martínez-Díaz, S. (2021) · Journal of Sensors · doi:10.1155/2021/5526931 · Cited by 15
A Low-cost Simultaneous Localization And Mapping Algorithm For Last-mile Indoor Delivery
Wang, W.-M., Zhao, W., Wang, X., Jin, Z.-H., Li, Y., & Runge, T. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883749 · Cited by 16
“At present, computer vision focus on Visual Simultaneous Localization and Mapping (SLAM) [8-11].”
BackgroundMode-enhanced space-time DIC: applications to ultra-high-speed imaging
Berny, M., Jailin, C., Bouterf, A., Hild, F., & Roux, S. (2018) · Measurement science and technology · doi:10.1088/1361-6501/aae3d5 · Cited by 16
“Images can also be processed through model reduction techniques in order to assess the best location for sensors and condense information to key points [30].”
MethodFerrera, M., Moras, J., Trouvé-Peloux, P., Creuze, V., & Dégez, D. (2018) · arXiv.org · doi:10.48550/arxiv.1809.07076 · Cited by 16
“Filtered SLAM based on Extended Kalman Filters (EKF) have been set aside to the profit of Bundle Adjustment (BA) based SLAM [5].”
MethodDyStSLAM: an efficient stereo vision SLAM system in dynamic environment
Li, X., Shen, Y.-H., Lu, J., Jiang, Q., Xie, O., Yang, Y., & Zhu, Q.-X. (2022) · Measurement science and technology · doi:10.1088/1361-6501/ac97b1 · Cited by 15
“It is based on an extended Kalman filter [14] and uses the current state of the camera and all landmarks as state quantities to update their mean and covariance.”
MethodMulti-Antenna Vision-and-Inertial-Aided CDGNSS for Micro Aerial Vehicle Pose Estimation
Yoder, J. E., Iannucci, P. A., Narula, L., & Humphreys, T. (2020) · Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) · doi:10.33012/2020.17588 · Cited by 15
“While optimization-based SLAM achieves better accuracy than filter-based SLAM for an equivalent amount of computation [33], ROVIO offers superior robustness for real-time flight control.”
MethodA Geometric Observer Design for Visual Localisation and Mapping
van Goor, P., Mahony, R. E., Hamel, T., & Trumpf, J. (2019) · IEEE Conference on Decision and Control · doi:10.1109/cdc40024.2019.9029435 · Cited by 15
“Additionally, these methods suffer from high computational complexity and poor scalability [1], [4].”
Mobile Robot Simultaneous Localization and Mapping Based on a Monocular Camera
Jia, S., Wang, K., & Li, X. (2016) · J. Robotics · doi:10.1155/2016/7630340 · Cited by 15
Rapid free-space mapping from a single omnidirectional camera
Lukierski, R., Leutenegger, S., & Davison, A. (2015) · European Conference on Mobile Robots · doi:10.1109/ecmr.2015.7324222 · Cited by 15
Self-citationAn homogeneous space geometry for simultaneous localisation and mapping
Mahony, R., Hamel, T., & Trumpf, J. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.04.012 · Cited by 15
“Filtering approaches exploit the temporal nature of the problem to fuse measurements sequentially into an “information” state which is then updated as new data is obtained (Strasdat, Montiel and Davison (2012)).”
BackgroundMethodSLAM Meets NeRF: A Survey of Implicit SLAM Methods
Yang, K., Cheng, Y., Chen, Z., & Wang, J. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15030085 · Cited by 13
Brief Announcement: Optimized GPU-accelerated Feature Extraction for ORB-SLAM Systems
Muzzini, F., Capodieci, N., Cavicchioli, R., & Rouxel, B. (2023) · ACM Symposium on Parallelism in Algorithms and Architectures · doi:10.1145/3558481.3591310 · Cited by 14
“Other approaches are Direct methods that use the image’s pixel without extracting features [6, 27] and keyframe based methods that select only a few keyframe to reconstruct the map [11, 22].”
LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry
Garcia-Fidalgo, E., J. P. Company-Corcoles, Bonnín-Pascual, F., & Ortiz, A. (2021) · Robotics Auton. Syst. · doi:10.48550/arxiv.2111.03393 · Cited by 14
“Following these ideas, Strasdat proposed a number of solutions [18, 19, 20] that served as a basis for ORB-SLAM [21, 22, 23], which can be considered as one of the most popular solutions in Visual SLAM.”
Smartphone-Based Indoor Visual Navigation with Leader-Follower Mode
Xu, J.-G., Dong, E.-Q., Ma, Q., Wu, C., & Yang, Z. (2021) · ACM Trans. Sens. Networks · doi:10.1145/3448417 · Cited by 14
“Later, optimization-based methods [27, 35] came on stage and were demonstrated to be more accurate [53] than a filtering-based approach.”
MethodA Semi-Direct Monocular Visual SLAM Algorithm in Complex Environments
Liang, Z.-W., & Wang, C.-Z. (2020) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-020-01297-8 · Cited by 14
Handheld 3D reconstruction based on closed-loop detection and nonlinear optimization
Hou, J.-Y., Yu, L., Li, C.-D., & Fei, S. (2019) · Measurement science and technology · doi:10.1088/1361-6501/ab4731 · Cited by 14
An Improved Augmented Reality Registration Method Based on Visual SLAM
Gao, Q.-H., Wan, T.-R., Tang, W., Chen, L., & Zhang, K.-B. (2017) · International Conference on E-learning and Games · doi:10.1007/978-3-319-65849-0_2 · Cited by 14
“Initially Simultaneous Localization and Mapping (SLAM), as a probability algorithm, has been mainly used for positioning robots in unknown environments [14] [3].”
BackgroundAutomotive navigation for mobile robots: Comprehensive review
Taleb, M. A., Korsoveczki, G., & Husi, G. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105837 · Cited by 13
Anderson Acceleration for on-Manifold Iterated Error State Kalman Filters
Gao, X., Xiao, T., Bai, C., Zhang, D., & Zhang, F. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3214056 · Cited by 13
Improving RGB-D SLAM in dynamic environments using semantic aided segmentation
Kenye, L., & Kala, R. (2021) · Robotica (Cambridge. Print) · doi:10.1017/s0263574721001521 · Cited by 13
“[4] shows that visual SLAM approaches which employs the graph-optimization approach outperforms the filtering-based approaches and graph-optimization approach is seen to be more popularly used in visual SLAM approaches.”
MethodFast stereo visual odometry based on LK optical flow and ORB-SLAM2
Tang, C.-Y., Zhao, X., Chen, J.-F., Chen, L., & Zhou, Y. (2020) · Multimedia Systems · doi:10.1007/s00530-020-00662-9 · Cited by 13
Gao, P., & Zhang, H. (2020) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra40945.2020.9196906 · Cited by 13
FMD Stereo SLAM: Fusing MVG and Direct Formulation Towards Accurate and Fast Stereo SLAM
Tang, F.-L., Li, H., & Wu, Y. (2019) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2019.8793664 · Cited by 13
“The pose of current frame is only optimized, which is known as motion-only BA [20].”
MethodGraph-Optimization-Based ZUPT/UWB Fusion Algorithm
Wang, Y., & Li, X. (2018) · ISPRS Int. J. Geo Inf. · doi:10.3390/ijgi7010018 · Cited by 13
“The largest difference between the graph optimization method and the Bayes filtering method is that the former makes no Markov assumption and directly maintains the previous statuses [15].”
MethodEfficient rotation estimation for 3D registration and global localization in structured point clouds
Ma, Y.-X., Guo, Y., Lei, Y.-J., Lu, M., & Zhang, J. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.09.003 · Cited by 13
Multifeature-based importance weighting for the PHD SLAM filter
Leung, K. Y. K., Inostroza, F., & Adams, M. D. (2016) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2016.150566 · Cited by 16
DCSLAM: A dynamically constrained real-time slam
Ramadasan, D., Chateau, T., & Chevaldonné, M. (2015) · International Conference on Information Photonics · doi:10.1109/icip.2015.7350976 · Cited by 13
“In this paper, we focus on bundle adjustment (BA) methods that offer a good trade-off between accuracy and execution time as shown by Strasdat et al. [3].”
Zhang, G., Vela, P., Tsiotras, P., & Cho, D. (2014) · AIAA SPACE Conference & Exposition · doi:10.2514/6.2014-4262 · Cited by 13
Active structure from motion for spherical and cylindrical targets
Spica, R., Giordano, P., & Chaumette, F. (2014) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2014.6907658 · Cited by 13
“A recent discussion about the pros/cons of both approaches in the context of Visual SLAM can be found in [1].”
BackgroundA Tutorial: Mobile Robotics, SLAM, Bayesian Filter, Keyframe Bundle Adjustment and ROS Applications
Aslan, M. F., Durdu, A., Yusefi, A., Sabancı, K., & Sungur, C. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-75472-3_7 · Cited by 13
Fast and Robust Semidirect Monocular Visual-Inertial Odometry for UAV
Zeng, Q.-X., Yu, H., Ji, X., Tao, X., & Hu, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3309907 · Cited by 12
“It was demonstrated in [17] that optimization-based methods can provide higher accuracy compared to filtered methods with the same computational cost.”
MethodRGB‐D SLAM with moving object tracking in dynamic environments
Dai, W.-C., Zhang, Y., Zheng, Y., Sun, D., & Li, P. (2021) · IET Cyber-Systems and Robotics · doi:10.1049/csy2.12019 · Cited by 12
“Since the latter are more accurate than the former most of the advanced visual methods fall into this category [16].”
BackgroundGood Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAMPreprint
Zhao, Y., Smith, J. S., & Vela, P. (2020) · arXiv.org · doi:10.48550/arxiv.2008.10123 · Cited by 12
A robust visual SLAM system in dynamic man-made environments
Liu, J., Meng, Z.-Y., & You, Z. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1602-3 · Cited by 12
Visual Multimodal Odometry: Robust Visual Odometry in Harsh Environments
Kleinschmidt, S., & Wagner, B. (2018) · IEEE International Symposium on Safety, Security and Rescue Robotics · doi:10.1109/ssrr.2018.8468653 · Cited by 12
“Keyframe-based techniques have shown to be more accurate than filtering approaches at the same computational costs [7].”
MethodAccurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
Narula, L., Wooten, J. M., Murrian, M. J., LaChapelle, D. M., & Humphreys, T. (2018) · Italian National Conference on Sensors · doi:10.3390/s18082452 · Cited by 12
Mobile Robot Localization: Where We Are and What Are the Challenges?
Skrzypczyński, P. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-54042-9_23 · Cited by 16
Dai, B., He, Y., Yang, L., Su, Y., Yue, Y., & Xu, W. (2020) · IEEE Access · doi:10.1109/access.2020.3000555 · Cited by 13
“Filter-based methods are advantageous when processing global sensors, but perform suboptimally compared to optimization-based methods [23].”
MethodA Visual Navigation Algorithm for UAV Based on Visual-Geography Optimization
Xu, W., Yang, D., Liu, J.-Y., Li, Y.-F., & Zhou, M.-A. (2024) · Drones · doi:10.3390/drones8070313 · Cited by 11
Degeneration-Aware Localization with Arbitrary Global-Local Sensor Fusion
Ding, X., Han, F.-Z., Yang, T., Wang, Y., & Xiong, R. (2021) · Italian National Conference on Sensors · doi:10.3390/s21124042 · Cited by 11
“Optimization-based methods are proven to be more accurate than filtering-based methods [21].”
MethodDense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDAR
Lin, X., Yang, B.-S., Wang, F.-H., Li, J.-P., & Wang, X.-Q. (2020) · International Journal of Digital Earth · doi:10.1080/17538947.2020.1862318 · Cited by 11
“Extensive studies have been investigated in 3D reconstruction of streetscape, which can be divided into the following categories: (1) simultaneous localization and mapping (SLAM), including the filterbased methods and the key-frame-based methods (Strasdat, Montiel, and Davison 2012).”
MethodInfluentialAbsolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR
Wang, Q., Tang, C., Dong, C., Mao, Q., Tang, F., Chen, J.-P., Hou, H.-Q., & Xiong, Y. (2020) · Italian National Conference on Sensors · doi:10.3390/s20030645 · Cited by 11
“With the assistance of laser/vision data, the simultaneous localization and mapping (SLAM) method can be utilized to complete positioning and attitude determination without GNSS signals [30,31].”
MethodCooperative SLAM using fuzzy Kalman filtering for a collaborative air-ground robotic system
Tsai, C.-C., Hsu, C., Lin, X.-C., & Tai, F.-C. (2019) · Zhongguó gongchéng xuékan · doi:10.1080/02533839.2019.1676657 · Cited by 11
“There are many classes of SLAM solutions including Kalman-fi lter-based (Mourikis and Roumeliotis 2007), particle-fi lter-based (Carlone et al. 2014), graph-based (Thrun and Montemerlo 2005) and visual SLAM SLAMs (Strasdat, Montiel, and Davison 2012; Engel, Koltun, and Cremers 2018).”
MethodHandling pure camera rotation in semi-dense monocular SLAM
Zhou, Y., Yan, F., & Zhou, Z. (2017) · The Visual Computer · doi:10.1007/s00371-017-1435-0 · Cited by 11
Building a grid-point cloud-semantic map based on graph for the navigation of intelligent wheelchair
Zhao, C., Hu, H.-S., & Gu, D.-B. (2015) · International Conference on Automation and Computing · doi:10.1109/iconac.2015.7313995 · Cited by 11
“Recently, the graph-based SLAM [9][10][11] achieved many breakthroughs, which constructs a graph out of the raw sensor measurements.”
Background
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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