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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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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
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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 201–300 of 426
Shi, Y., Ji, S.-P., Shao, X.-W., Yang, P., Wu, W.-B., Shi, Z.-C., & Shibasaki, R. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.06.002 · Cited by 11
Unscented iSAM: A consistent incremental solution to cooperative localization and target tracking
Huang, G., Truax, R., Kaess, M., & Leonard, J. (2013) · European Conference on Mobile Robots · doi:10.1109/ecmr.2013.6698850 · Cited by 11
An Artificial Neural SLAM Framework for Event-Based Vision
Gelen, A. G., & Atasoy, A. (2023) · IEEE Access · doi:10.1109/access.2023.3282637 · Cited by 11
“In the paper by Strasdat et al., the two approaches were compared and the superiority of the keyframe and optimization-based method over the filter-based methods was emphasized [1].”
MethodAdaptive feature-conserving compression for large scale point clouds
Eickeler, F., Sánchez‐Rodríguez, A., & Borrmann, A. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101236 · Cited by 11
A Survey on the Main Techniques Adopted in Indoor and Outdoor Localization
Stefanoni, M., Kovács, I., Sarcevic, P., & Odry, Á. (2025) · Electronics · doi:10.3390/electronics14102069 · Cited by 10
A flexible trajectory estimation methodology for kinematic laser scanning
Pöppl, F., Ullrich, A., Mandlburger, G., & Pfeifer, N. (2024) · Isprs Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.06.014 · Cited by 10
Structure from Motion-Based Mapping for Autonomous Driving: Practice and Experience
Zhanabatyrova, A., Leite, C., & Xiao, Y. (2023) · ACM Trans. Internet Things · doi:10.1145/3631533 · Cited by 10
An Energy Efficient and Runtime Reconfigurable Accelerator for Robotic Localization
Liu, Q., Hao, Y.-H., Liu, W.-Z., Yu, B., Gan, Y.-M., Tang, J., Liu, S.-S., & Zhu, Y. (2023) · IEEE transactions on computers · doi:10.1109/tc.2022.3230899 · Cited by 10
“Principled mathematical solutions for SLAM include filtering-based [3] and non-linear optimization-based [2], [4], where the latter recently shows higher robustness but with intensive computation.”
MethodRAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry
Cimarelli, C., Bavle, H., Sánchez-López, J. L., & Voos, H. (2022) · Sensors · doi:10.3390/s22072651 · Cited by 9
“[25] show that the accuracy of the pose increases when the SLAM system tracks more features and that the computational cost for filtering is cubic in the number of features’ observations compared to linear for BA.”
BackgroundGraph-optimisation-based self-calibration method for IMU/odometer using preintegration theory
Bai, S.-Y., Lai, J., Lyu, P., Cen, Y., Wang, B.-Q., & Sun, X. (2022) · Journal of navigation · doi:10.1017/s0373463321000722 · Cited by 10
“In recent years, the graph-optimisation theory has been widely used in the robotics field (Strasdat et al., 2012; Mascaro et al., 2018).”
MethodRGB-D SLAM in Dynamic Environments Using Points CorrelationsPreprint
Dai, W.-C., Zhang, Y., Li, P., & Fang, Z. (2018) · arXiv.org · doi:10.48550/arxiv.1811.03217 · Cited by 10
“Most recent advanced visual systems are using optimization methods [19], which can be divided into two categories: indirect methods [1], [9], [10] and direct methods [5]–[7].”
BackgroundMultiple Maps for the Feature-based Monocular SLAM System
Hu, H., Sun, H.-X., Ye, P., Jia, Q.-X., & Gao, X. (2018) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-018-0829-1 · Cited by 10
Summan, R., Dobie, G., West, G., Marshall, S., Macleod, C., & Pierce, S. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2723728 · Cited by 10
A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping
Munguía, R., Trujillo, J.-C., Guerra, E., & Grau, A. (2021) · Italian National Conference on Sensors · doi:10.3390/s22010210 · Cited by 9
“This result is consistent with the one presented in [31] in which filter-based SLAM methods are compared with optimization-based SLAM methods.”
Deep Reinforcement Learning-Based Robot Exploration for Constructing Map of Unknown Environment
Chen, S.-Y., He, Q.-F., & Lai, C.-F. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10218-5 · Cited by 9
“The Strasdat team proved that key-frame-based methods are more accurate than the filters method at the same computational cost (Strasdat et al., 2012).”
Zhong, S., & Chirarattananon, P. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3047775 · Cited by 9
“While optimization-based approaches often outperform filter-based methods in terms of accuracy [8], systems employing EKF frameworks [5]–[7] possess superior efficiency.”
MethodIndirect Visual Simultaneous Localization and Mapping Based on Linear Models
Chien, C.-H., Hsu, C., Wang, W.-Y., & Chiang, H.-H. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2952722 · Cited by 9
An Equivariant Observer Design for Visual Localisation and Mapping
van Goor, P., Mahony, R., Hamel, T., & Trumpf, J. (2019) · arXiv.org · doi:10.48550/arxiv.1904.02452 · Cited by 9
“State-of-the-art filters and observers do not deal well with the inherent nonlinearity of the SLAM problem [2], and suffer from high computational complexity and poor scalability [1], [4].”
BackgroundA monocular ORB-SLAM in dynamic environments
Cui, L.-Y., & Wen, F. (2019) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1168/5/052037 · Cited by 9
“al.[2] proved that keyframe bundle adjustment obtains more accurate result than filtering methods in the same consuming time.”
MethodSDVL: Efficient and Accurate Semi-Direct Visual Localization
Perdices, E., & Cañas, J. (2019) · Italian National Conference on Sensors · doi:10.3390/s19020302 · Cited by 9
“They succesfully handle thousands of points [5], which provides robustness to the estimation.”
BackgroundA Multi-User Personal Indoor Localization System Employing Graph-Based Optimization
Nowicki, M. R., & Skrzypczyński, P. (2019) · Italian National Conference on Sensors · doi:10.3390/s19010157 · Cited by 9
A Diversified Generative Latent Variable Model for WiFi-SLAM
Xiong, H., & Tao, D. (2017) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11038 · Cited by 9
Single camera absolute motion based digital elevation mapping for a next generation planetary lander
Feetham, L., Aouf, N., Bourdarias, C., & Voirin, T. (2014) · Acta Astronautica · doi:10.1016/j.actaastro.2014.01.011 · Cited by 9
UNVEILING LARGE-SCALE HISTORICAL CONTENTS WITH V-SLAM AND MARKERLESS MOBILE AR SOLUTIONS
Torresani, A., Rigon, S., Farella, E., Menna, F., & Remondino, F. (2021) · The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences · doi:10.5194/isprs-archives-xlvi-m-1-2021-761-2021 · Cited by 9
Qayyum, U., Ahsan, Q., & Mahmood, Z. (2017) · International Bhurban Conference on Applied Sciences and Technology · doi:10.1109/ibcast.2017.7868075 · Cited by 9
“A monocular camera based approaches have been proposed by number of researchers, however it suffers from a scale drift problem [2].”
Background3D Gaussian splatting technologies and extensions: A review
Luan, F., Sun, S., Zhang, H., Yin, Y., Wang, K., & Yang, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131629 · Cited by 8
Gu, P.-F., Meng, Z.-Y., & Zhou, P.-K. (2022) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros47612.2022.9981598 · Cited by 8
“It is shown that optimization-based methods have superior accuracy and robustness at the expense of heavy computational burden [10].”
Stereovision-Based Ego-Motion Estimation for Combine Harvesters
Chen, H., Chen, J., Guan, Z.-H., Li, Y.-M., Cheng, K., & Cui, Z.-H. (2022) · Italian National Conference on Sensors · doi:10.3390/s22176394 · Cited by 8
“Hauke Strasdat’s [19] research showed that, through progress in computer processing power and algorithms, keyframe-based bundle adjustment outperformed filtering.”
BackgroundSimultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization
Saxena, A., Chiu, C.-Y., Shrivastava, R., Menke, J., & Sastry, S. (2021) · IEEE Robotics and Automation Letters · doi:10.48550/arxiv.2112.05921 · Cited by 8
“Strasdat et al. conducted Monte Carlo experiments on visual SLAM algorithms [19], revealed that including more features in the back end increased accuracy more (compared to including more frames), and concluded that bundle adjustment (BA) outperforms filtering, since its computation time increases…”
BackgroundLeung, R., Balamurali, M., & Lowe, A. (2021) · Mathematical Geosciences · doi:10.1007/s11004-021-09967-5 · Cited by 8
“Similar reasoning is used in SLAM (simultaneous localization and mapping) problems in robotics navigation [39] where filtering methods marginalize out past poses.”
MethodAMADEE-18: Vision-Based Unmanned Aerial Vehicle Navigation for Analog Mars Mission (AVI-NAV)
Allak, E., Brommer, C., Dallenbach, D., & Weiss, S. (2020) · Astrobiology · doi:10.1089/ast.2019.2036 · Cited by 8
Chen, Y., Zhang, M., Hong, D., Deng, C.-C., & Li, M. (2019) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros40897.2019.8968078 · Cited by 8
“Comparison of two families of algorithms are widely discussed recently [31], and it is not fair to draw an easy conclusion that one outperforms the other completely.”
BackgroundVisual SLAM incorporating wheel odometer for indoor robots
Wang, J., Shi, Z.-Y., & Zhong, Y.-S. (2017) · Cybersecurity and Cyberforensics Conference · doi:10.23919/chicc.2017.8028171 · Cited by 8
“It has been shown by Strasdat [8] that such optimization based methods is more efficient compared to EKF based methods.”
MethodMulti-sensor fusion techniques for state estimation of micro air vehicles
Donavanik, D., Hardt-Stremayr, A., Gremillion, G. M., Weiss, S., & Nothwang, W. (2016) · Defense + Security · doi:10.1117/12.2224162 · Cited by 8
“Using only a monocular camera, bundle adjustment outperforms filter-based approaches based on precision per computing time for a large enough amount of processing resources.(7) When integrating the IMU pose with the camera pose, which also known as sensor fusion, such a general statement is not yet available.”
BackgroundMethodInfluentialThe EKF-Based Visual SLAM System with Relative Map Orientation Measurements
Schmidt, A. (2014) · International Conference on Computer Vision and Graphics · doi:10.1007/978-3-319-11331-9_68 · Cited by 8
Real-Time Accurate Geo-Localization of a MAV with Omnidirectional Visual Odometry and GPS
Schneider, J., & Förstner, W. (2014) · ECCV Workshops · doi:10.1007/978-3-319-16178-5_18 · Cited by 8
Uncontrolled Two-Step Iterative Calibration Algorithm for Lidar–IMU System
Yin, S., Xie, D., Fu, Y., Wang, Z., & Zhong, R.-F. (2023) · Italian National Conference on Sensors · doi:10.3390/s23063119 · Cited by 7
“Traditional methods were primarily implemented through filtering [5], and classical algorithms were utilized to accomplish localization and mapping through the Extended Kalman Filter (EKF) [4].”
MethodSystematic literature review of collaborative SLAM applied to autonomous mobile robots
Aloui, K., Guizani, A., Hammadi, M., Haddar, M., & Soriano, T. (2022) · 2022 IEEE Information Technologies & Smart Industrial Systems (ITSIS) · doi:10.1109/itsis56166.2022.10118378 · Cited by 7
“Based on the literature [20] to enhance the precision of vSLAM, it is necessary to expand the set of minutiae in a card.”
MethodTriplet loss based metric learning for closed loop detection in VSLAM system
Chang, J., Dong, N., Li, D., & Qin, M.-C. (2021) · Expert systems with applications · doi:10.1016/j.eswa.2021.115646 · Cited by 7
Visual-inertial teach and repeat
Nitsche, M., Pessacg, F., & Civera, J. (2020) · Robotics Auton. Syst. · doi:10.1016/j.robot.2020.103577 · Cited by 7
Monocular Visual-Inertial-Pressure fusion for Underwater localization and 3D mapping.
Ferrera, M. (2019) · theses.fr (ABES) · doi:10.70675/0a530b06ze9c8z4a9dzabccz9acc11efeb31 · Cited by 9
“Bundle Adjustment is more costly than a single iteration in an EKF but provides better estimates as it iteratively refines its estimations (Strasdat et al., 2012).”
BackgroundThe CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better
Parker, C., Daiter, M., Omar, K., Levi, G., & Hassner, T. (2016) · ECCV Workshops · doi:10.1007/978-3-319-49409-8_57 · Cited by 7
CLAM: Coupled Localization and Mapping with Efficient Outlier Handling
Balzer, J., & Soatto, S. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.204 · Cited by 7
Monocular-Based SLAM for Mobile Robots: Filtering-Optimization Hybrid Approach
Munguía, R., Trujillo, J.-C., Obregón-Pulido, G., & Aldana, C. I. (2023) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-023-01981-5 · Cited by 8
Online Mutual Adaptation of Deep Depth Prediction and Visual SLAMPreprint
Loo, S., Shakeri, M., Tang, S., Mashohor, S., & Zhang, H. (2021) · arXiv.org · doi:10.48550/arxiv.2111.04096 · Cited by 6
“BA [15] and pose-graph optimization [16], on the established”
BackgroundDaneshgaran, F., Marangi, A., Bruno, N., Lizzio, F., Mondin, M., & Olia, K. (2020) · Transportation Research Record · doi:10.1177/0361198120963109 · Cited by 6
Lane-level localization system using surround-view cameras adaptive to different driving conditions
Li, T., Qian, Y., Fortelle, A. D. L., Chan, C.-Y., & Wang, C.-X. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420921630 · Cited by 6
Visual-Inertial Teach & Repeat for Aerial Robot Navigation
Nitsche, M., Pessacg, F., & Civera, J. (2019) · European Conference on Mobile Robots · doi:10.1109/ecmr.2019.8870926 · Cited by 6
“Including this term is beneficial under a motiononly estimation and represents better the resulting pose uncertainty [14], [4].”
BackgroundA Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System
Jiang, J., Niu, X., Guo, R., & Liu, J.-N. (2019) · Italian National Conference on Sensors · doi:10.3390/s19153418 · Cited by 6
Visual SLAM-based Localization and Navigation for Service Robots: The Pepper Case
Gómez, C., Mattamala, M., Resink, T., & Ruiz-del-Solar, J. (2018) · Robot Soccer World Cup · doi:10.1007/978-3-030-27544-0_3 · Cited by 6
“Being originally formulated as a filtering problem, nowadays optimization-based approaches are preferable by its superior accuracy at similar computational cost [13].”
BackgroundStereo vision-based simultaneous localization and mapping with ranging aid
Lee, Y.-H., Zhu, C., Giorgi, G., & Günther, C. (2018) · 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) · doi:10.1109/plans.2018.8373407 · Cited by 6
A hybrid feature parametrization for improving stereo-SLAM consistency
Zhang, H., Hasith, K., & Wang, H. (2017) · 2017 13th IEEE International Conference on Control & Automation (ICCA) · doi:10.1109/icca.2017.8003201 · Cited by 6
“In recent years keyframe-based approach has dominated this field for its better consistency and accuracy [15].”
BackgroundObserver design for position and velocity bias estimation from a single direction output
Bras, F. L., Hamel, T., Mahony, R., & Samson, C. (2015) · IEEE Conference on Decision and Control · doi:10.1109/cdc.2015.7403428 · Cited by 6
“Most of the recent structure from motion algorithms are formulated as an optimisation problem over a set of selected images [2], however, recent work has emphasised the importance of considering motion models and filtering techniques [3] for a class of important problems.”
MethodHigh-Order Regularization Dealing With ILL-Conditioned Robot Localization Problems
Liu, X., & Cao, M. (2024) · IEEE Transactions on robotics · doi:10.1109/tro.2025.3562487 · Cited by 5
“Theuseofregularization invisiontasks,suchasfeaturedetectionandmatching,3-”
Robust Absolute Headset Tracking for Extended Reality
Tenny, R. M., Sun, L. C., Duru, A., & Humphreys, T. (2023) · 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS) · doi:10.1109/plans53410.2023.10140004 · Cited by 5
“In terms of computational complexity, the authors of [18] discovered that with N feature points and M keyframes, filtering SLAM is O ( MN 3 ) whereas BA SLAM is O ( NM 2 + M 3 ) ; importantly, the authors also show that an increase in feature points increases accuracy while an increase in keyframes…”
BackgroundMethodContinuous-Time Trajectory Estimation for Differentially Flat Systems
Johnson, J. C., Mangelson, J. G., & Beard, R. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3224364 · Cited by 5
“This shift has largely been driven by the finding that, in the case of visual simultaneous localization and mapping (SLAM), MAP methods offer more accuracy per unit of computation than EKF-based approaches [2].”
MethodVisual Odometry for Self-Driving with Multihypothesis and Network Prediction
Chen, Y., Mei, Y., & Wan, S.-F. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/1930881 · Cited by 4
Mobile Robot Localization Based on Vision and Multisensor
Yao, L., & Li, F. (2020) · J. Robotics · doi:10.1155/2020/8701619 · Cited by 5
Improved SLAM closed-loop detection algorithm based on DBoW2
Zhang, Q., Xu, G., & Li, N. (2019) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1345/4/042094 · Cited by 5
“…by extracting the features of the visual image training set. leaf independent the determine of the adopted as then and easily the get − million dictionary determine , and can achieve 7] , and produces structure of by of the clustering seriously , the clustering k-means form of in this be improved”
BackgroundMethodDirect-ORB-SLAM: Direct Monocular ORB-SLAM
Cui, L.-Y., Ma, C.-W., & Wen, F. (2019) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1345/3/032016 · Cited by 5
“But the MonoSLAM is based on Extended Kalman Filter (EKF) which is proved less accurate than non-linear optimization at the same computational cost [9].”
MethodOriginal Loop-closure Detection Algorithm for Monocular vSLAM
Bokovoy, A., & Yakovlev, K. (2017) · International Joint Conference on the Analysis of Images, Social Networks and Texts · doi:10.1007/978-3-319-73013-4_19 · Cited by 5
3D reconstruction quality analysis and its acceleration on GPU clusters
Polok, L., Ila, V., & Smrz, P. (2016) · European Signal Processing Conference · doi:10.1109/eusipco.2016.7760420 · Cited by 5
Gyroscope assisted scalable visual simultaneous localization and mapping
Babu, B. P. W., Cyganski, D., & Duckworth, J. (2014) · Ubiquitous Positioning, Indoor Navigation, and Location Based Service · doi:10.1109/upinlbs.2014.7033731 · Cited by 7
“Tracking a greater number of features in the environment gives better accuracy[18].”
BackgroundA ROS Implementation of the Mono-Slam Algorithm
Russo, L., Rosa, S., Bona, B., & Matteucci, M. (2014) · IEEE Annual Symposium on Foundations of Computer Science · doi:10.5121/csit.2014.4131 · Cited by 5
“The most evident limitation of the algorithm is the fact that, when the map becomes too large, the EKF processing phase becomes too computationally heavy to be computed in real- time [21].”
BackgroundA New Real-Time Visual SLAM Algorithm Based on the Improved FAST Features
Wang, L., Liu, R., Liang, C., & Duan, F. (2013) · Interacción · doi:10.1007/978-3-642-39342-6_23 · Cited by 5
Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
Choi, S.-K., Park, D., Hwang, S.-Y., & Kim, T.-W. (2025) · arXiv.org · doi:10.48550/arxiv.2510.01648 · Cited by 4
“However, one potential limitation in many approaches is the reliance on static noise models, which may not fully capture the dynamic nature of measurement uncertainty [7].”
Li, X., Wu, F., Chang, H., Li, X.-X., Tan, Y., Shen, Z., & Zhang, X. (2025) · GPS Solutions · doi:10.1007/s10291-025-01893-x · Cited by 4
“…advancements in computing technology and autonomous systems have underscored the effectiveness of Factor Graph Optimization (FGO) as a parameter estimation framework, particularly in Simultaneous Localization and Mapping (SLAM) applications (Strasdat et al. 2012; Leutenegger et al. 2015; Qin 2018).”
Control and monocular visual SLAM of nonholonomic mobile robots
Trujillo, J.-C., Munguia, R., Albarrán, J., & Arteaga, M. (2024) · European Journal of Control · doi:10.1016/j.ejcon.2024.101171 · Cited by 4
Visual-inertial-actuator odometry for multirotor UAVs with rotor drag and external disturbance
Moeini, A., Lynch, A. F., & Zhao, Q. (2024) · International Journal of Dynamics and Control · doi:10.1007/s40435-023-01357-5 · Cited by 4
Latency vs precision: Stability preserving perception scheduling
Aldana-López, R., Aragüés, R., & Sagüés, C. (2023) · at - Automatisierungstechnik · doi:10.1016/j.automatica.2023.111123 · Cited by 4
“The setting in which different perception modes are available, achieving different noise levels and perception latency, captures the actual practical behavior of usual perception algorithms when different configurations are available [6,30,14,36].”
BackgroundMethodResultMap Point Selection for Visual SLAM
Müller, C. J., & van Daalen, C. V. (2023) · Robotics Auton. Syst. · doi:10.1016/j.robot.2023.104485 · Cited by 4
“[16] showed that bundle adjustment results in more accurate trajectory estimates for visual SLAM than filtering at the same computational budget.”
BackgroundFast Bayesian graph update for SLAM
Taguchi, S., Deguchi, H., Hirose, N., & Kidono, K. (2022) · Advanced Robotics · doi:10.1080/01691864.2021.2013939 · Cited by 5
“However, in recent VSLAM, Bayesian-filtering-based approaches have lagged behind bundle-adjustment-based (BA-based) approaches in terms of accuracy and computation cost of handling a large number of image features for local pose estimation [37].”
BackgroundAdvanced Visual SLAM and Image Segmentation Techniques for Augmented Reality
Jiang, Y.-R., Tran, T., & Williams, L. (2022) · International Journal of Virtual and Augmented Reality · doi:10.4018/ijvar.307063 · Cited by 4
Xie, X., Yang, T., Zhang, Y.-N., Liang, B., & Liu, L.-F. (2021) · IET Computer Vision · doi:10.1049/cvi2.12053 · Cited by 4
Elankumaran, K., & Dempster, A. (2021) · Acta Astronautica · doi:10.1016/j.actaastro.2021.01.044 · Cited by 4
Globally stable velocity estimation using normalized velocity measurement
Bjørne, E. S., Brekke, E., Bryne, T., Delaune, J., & Johansen, T. (2019) · Int. J. Robotics Res. · doi:10.1177/0278364919887436 · Cited by 4
“A further discussion of the pros and cons between the methods can be seen in [5].”
MethodAtchuthan, D. (2018) · doi:10.70675/cca5615dz133az4b0bz93bdz32710533daaa · Cited by 4
Research on service robots robust relocalization algorithm based on 2D/3D map of indoor environment
Wang, L., Li, R.-F., Zhao, L.-J., Hou, Z., Li, X.-Y., & Sun, Z. (2017) · International Conference on Advanced Robotics · doi:10.1109/icar.2017.8023668 · Cited by 4
“On account of the limitations and other issues of the filter method, methods based on the optimization have become the mainstream of the current visual SLAM research [1].”
MethodImproved KinectFusion based on graph-based optimization and large loop model
Jia, S., Li, B., Zhang, G.-L., & Li, X. (2016) · International Conference on Informatics and Analytics · doi:10.1109/icinfa.2016.7831931 · Cited by 4
Monocular Camera based Trajectory Tracking of 3‐DOF Helicopter
Li, S., Liu, H., Shi, Z.-Y., & Zhong, Y.-S. (2014) · doi:10.1002/asjc.857 · Cited by 4
“Compared with the filter-based vSLAM technique [17] or the FastSLAM technique [19], the PTAM algorithm will provide a more accurate result [24], owing to the great quantity of feature points and the nonlinear batch optimization.”
MethodImproving visual SLAM accuracy through deliberate camera oscillations
Heshmat, M., Abdellatif, M., & Abbas, H. S. (2013) · International Symposium on Robotic and Sensors Environments · doi:10.1109/rose.2013.6698435 · Cited by 4
“There are many differences in computations side, but on the accuracy side, filtering techniques may reach comparable accuracy as that of BA [9].”
BackgroundResultSAMSLAM: Simulated Annealing Monocular SLAM
Fanfani, M., Bellavia, F., Pazzaglia, F., & Colombo, C. (2013) · International Conference on Computer Analysis of Images and Patterns · doi:10.1007/978-3-642-40246-3_64 · Cited by 4
Autonomous Navigation Using Monocular ORB SLAM2
Vithalani, S., Soni, S., & Rajpura, P. (2020) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-15-0974-2_6 · Cited by 4
MCSLAM: a Multiple Constrained SLAM
Ramadasan, D., Chevaldonné, M., & Chateau, T. (2015) · British Machine Vision Conference · doi:10.5244/c.29.107 · Cited by 4
A New Tightly-Coupled Dual-VIO for a Mobile Manipulator With Dynamic Locomotion
Xu, J., & Jeon, S. (2024) · IEEE/ASME transactions on mechatronics · doi:10.1109/tmech.2024.3400918 · Cited by 3
“These anchor points provide a soft geometric constraint during the VIO pose optimization.”
BackgroundVI-HSO: Hybrid Sparse Monocular Visual-Inertial Odometry
Yang, W., Zhuang, Y., Luo, D., Wang, W., & Zhang, H. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3305238 · Cited by 3
“Optimization-based methods can have higher accuracy than filtering-based ones [20].”
MethodHigh-frame rate homography and visual odometry by tracking binary features from the focal plane
Murai, R., Saeedi, S., & Kelly, P. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10122-8 · Cited by 3
“After every keyframe insertion, structure-only bundle adjustment (Strasdat et al., 2012) is carried out to refine the 3D map.”
MethodOn IMU preintegration: A nonlinear observer viewpoint and its application
Yi, B., & Manchester, I. (2023) · Systems & control letters (Print) · doi:10.48550/arxiv.2307.04165 · Cited by 3
PEBO-SLAM: Observer Design for Visual Inertial SLAM With Convergence Guarantees
Yi, B., Jin, C., Wang, L., Shi, G., Ila, V., & Manchester, I. R. (2024) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2024.3466874 · Cited by 3
Fu, S.-Q., Yao, Z., Guo, J., & Wang, H. (2022) · 2022 IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9856015 · Cited by 3
“In addition, it is the first system to utilize nonlinear optimization in the mapping thread, which provides better accuracy than filter-based methods with the same computational cost [19].”
MethodImproved 2D laser slam graph optimization based on Cholesky decomposition
Gao, L., Dong, C.-Y., Liu, X.-Y., Ye, Q., Zhang, K., & Chen, X.-Y. (2022) · International Conference on Control, Decision and Information Technologies · doi:10.1109/codit55151.2022.9803938 · Cited by 3
“As researchers gradually realize the sparsity of Jacobian matrix in LSLAM back-end optimization [9], the method of nonlinear optimization has become the mainstream method of LSLAM [10].”
MethodToward Efficient and Robust Multiple Camera Visual-inertial OdometryPreprint
He, Y., Yu, H., Yang, W., & Scherer, S. (2021) · arXiv.org · doi:10.48550/arxiv.2109.12030 · Cited by 3
“ROVIO has the least CPU usages (around 60%), but the back-end EKF is less efficient compared to the bundle adjustment method [13].”
Moving Object Tracking for SLAM-based Augmented Reality
de Oliveira, D. C. B., & da Silva, R. L. D. S. (2021) · J. Mobile Multimedia · doi:10.13052/jmm1550-4646.1745 · Cited by 3
Multi-Parameter Optimization for a Robust RGB-D SLAM System
Wang, Y.-Z., Zhu, X.-X., He, G., & Cao, Q.-X. (2021) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra48506.2021.9561538 · Cited by 3
“Some algorithms without keyframes proved insufficient compared with keyframe-based ones, becoming the gold standard in visual SLAM [13].”
MethodCEB-Map: Visual Localization Error Prediction for Safe Navigation
Chen, W., Zhu, L., Wang, C.-Q., He, L., & Meng, M. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2999641 · Cited by 3
“Regarding the belief map based on vision information, exploiting the map built by keyframe-based VSLAM (Visual Simultaneous Localization and Mapping) [11], [12], the belief map considering both the distribution and precision of landmarks (Fig.”
BackgroundMethodA positioning method of intelligent plastering robot head
Liu, Y., & Yu, H.-Y. (2020) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid51228.2020.00077 · Cited by 3
“The roof and ground often have unevenness problems, which may make the fixed rod not in the ideal parallel position, so that the plastering machine is not parallel to the wall before plastering[8].”
BackgroundA Remote RGB-D VSLAM Solution for Low Computational Powered Robots
Alves, J., & Bernardino, A. (2020) · 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) · doi:10.1109/icarsc49921.2020.9096074 · Cited by 3
“The system is keyframe-based [13] and performs BA [12] optimization to estimate camera pose (motion-only BA), to perform an optimal reconstruction of the camera surroundings (local BA) and to achieve the optimal solution after pose-graph optimization when loop-closure is necessary (Full BA).”
MethodStudy on the method of SLAM initialization for monocular vision
Xu, G., Zhang, Q., & Li, N. (2019) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/569/5/052085 · Cited by 3
Directional Endpoint-based Enhanced EKF-SLAM for Indoor Mobile Robots
Gao, H.-M., Zhang, X., Li, C., Chen, X., Fang, Y.-C., & Chen, X. (2019) · 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim.2019.8868577 · Cited by 3
Allak, E., Hardt-Stremayr, A., & Weiss, S. (2018) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros.2018.8594170 · Cited by 3
“BA based algorithms keep a measurement history and optimize over the whole history while filter-based algorithms only consider the latest measurement [5].”
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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