The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback
Open accessAbstract
This paper presents a visual-inertial odometry framework that tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter. By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of noncorner-shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach, which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it with other state-of-the-art visual-inertial frameworks. Experimental results show that robust localization with high accuracy can be achieved with this filter-based framework.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“While numerous datasets aimed at vision-aided odometry exist [39], [40], [41], [42], [43], [44], few heterogeneous datasets of sizes suitable for deep learning applications on RGB-D data are available.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [40]
Citations
Cites
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
(1981) · Cited by 11,963
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 4×
“If we minimize the squared error terms for a multilevel patch, we obtain a patch alignment algorithm which is very similar to the well-known Kanade-Lucas-Tomasi (KLT) feature tracker (Lucas and Kanade (1981); Shi and Tomasi (1994)).”
MethodInfluentialProceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
LSD-SLAM: Large-scale direct monocular SLAM
Engel, J., Schöps, T., & Cremers, D. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10605-2_54 · Cited by 3,851
SVO: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906584 · Cited by 2,205
The EuRoC micro aerial vehicle datasets
Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M., & Siegwart, R. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915620033 · Cited by 2,086
Self-citationA Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation
Mourikis, A. I., & Roumeliotis, S. I. (2007) · Proceedings 2007 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2007.364024 · Cited by 1,951
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(2001) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2001.990916 · Cited by 1,884
Research works printed but not linked: 1
- Bloesch M, 2016, arXiv
Cited by
Sorted by citations
ORB-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., & Tardos, J. D. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3075644 · Cited by 4,676
FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
Xu, W. D., & Zhang, F. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3064227 · Cited by 1,159
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Joshi, B., Rahman, S., Kalaitzakis, M., Cain, B., Johnson, J. V., Xanthidis, M., Karapetyan, N., Hernandez, A., Li, A. Q., Vitzilaios, N. I., et al. (2019) · doi:10.1109/iros40897.2019 · Cited by 1,091
OpenVINS: A Research Platform for Visual-Inertial Estimation
Geneva, P., Eckenhoff, K., Lee, W., Yang, Y., & Huang, G. P. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196524 · Cited by 711
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
Schubert, D., Goll, T., Demmel, N., Usenko, V., Stückler, J., & Cremers, D. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593419 · Cited by 497
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation
Qin, C., Ye, H.-Y., Pranata, C. E., Han, J., Zhang, S.-Y., & Liu, M. (2019) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra40945.2020.9197567 · Cited by 353
Visual-Inertial Navigation: A Concise Review
Huang, G. P. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793604 · Cited by 401
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features
He, Y., Zhao, J. Y., Guo, Y., He, W., & Yuan, K. (2018) · Sensors · doi:10.3390/s18041159 · Cited by 331
An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments
Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R., & Nieto, J. I. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969191 · Cited by 311
Self-citationVisual-Inertial Mapping With Non-Linear Factor Recovery
Usenko, V., Demmel, N., Schubert, D., Stuckler, J., & Cremers, D. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2961227 · Cited by 250
A Survey on Odometry for Autonomous Navigation Systems
Mohamed, S., Haghbayan, M., Westerlund, T., Heikkonen, J., Tenhunen, H., & Plosila, J. (2019) · IEEE Access · doi:10.1109/access.2019.2929133 · Cited by 213
VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots
Wisth, D., Camurri, M., & Fallon, M. F. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3193788 · Cited by 183
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Chen, C., Rosa, S., Miao, Y., Lu, C. X., Wu, W., Markham, A., & Trigoni, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01079 · Cited by 178
Khattak, S., Nguyen, H., Mascarich, F., Dang, T., & Alexis, K. (2020) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas48674.2020.9213865 · Cited by 152
Guerra, W., Tal, E. A., Murali, V., Ryou, G., & Karaman, S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968116 · Cited by 139
Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey
Zhu, J., Li, H., & Zhang, T. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010010 · Cited by 141
Chen, C., Zhu, H. J., Li, M., & You, S. (2018) · Robotics · doi:10.3390/robotics7030045 · Cited by 131
SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
Rahman, S., Li, A. Q., & Rekleitis, I. M. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967703 · Cited by 131
A 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
IC-GVINS: A Robust, Real-Time, INS-Centric GNSS-Visual-Inertial Navigation System
Niu, X., Tang, H., Zhang, T., Fan, J. F., & Liu, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3224367 · Cited by 124
SelfVIO: Self-supervised deep monocular Visual–Inertial Odometry and depth estimation
Almalıoğlu, Y., Turan, M., Saputra, M. R. U., de Gusmão, P. P. B., Markham, A., & Trigoni, N. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.005 · Cited by 127
Tranzatto, M., Mascarich, F., Bernreiter, L., Godinho, C., Camurri, M., Khattak, S., Dang, T., Reijgwart, V., Loeje, J., Wisth, D., et al. (2022) · Field Robotics · doi:10.55417/fr.2022011 · Cited by 118
Self-citationJoshi, B., Rahman, S., Kalaitzakis, M., Cain, B., Johnson, J. V., Xanthidis, M., Karapetyan, N., Hernandez, A., Li, A. Q., Vitzilaios, N. I., et al. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968049 · Cited by 113
SVIn2: A multi-sensor fusion-based underwater SLAM system
Rahman, S., Li, A. Q., & Rekleitis, I. M. (2022) · The International Journal of Robotics Research · doi:10.1177/02783649221110259 · Cited by 112
State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems
Alkendi, Y., Seneviratne, L. D., & Zweiri, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3082778 · Cited by 114
History-Aware Autonomous Exploration in Confined Environments Using MAVs
Witting, C., Fehr, M., Behnemann, R., Oleynikova, H., & Siegwart, R. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594502 · Cited by 106
Self-citationClosed-form preintegration methods for graph-based visual–inertial navigation
Eckenhoff, K., Geneva, P., & Huang, G. P. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919835021 · Cited by 101
Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps
Reijgwart, V., Millane, A., Oleynikova, H., Siegwart, R., Cadena, C., & Nieto, J. I. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2953859 · Cited by 99
Self-citationRobust Real-time LiDAR-inertial Initialization
Zhu, F., Ren, Y., & Zhang, F. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982225 · Cited by 99
Autonomous aerial robotics for package delivery: A technical review
Saunders, J., Sajad Saeedi, G., & Li, W. (2023) · Journal of Field Robotics · doi:10.1002/rob.22231 · Cited by 109
ADVIO: An Authentic Dataset for Visual-Inertial Odometry
Cortés, S., Solin, A., Rahtu, E., & Kannala, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_26 · Cited by 98
Robocentric Visual-Inertial Odometry
Huai, Z., & Huang, G. P. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593643 · Cited by 97
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion
Düzçeker, A., Galliani, S., Vogel, C., Speciale, P., Dusmanu, M., & Pollefeys, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01507 · Cited by 96
Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle
Jeon, J., Jung, S., Lee, E. M., Choi, D., & Myung, H. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3075141 · Cited by 96
The Urban Last Mile Problem: Autonomous Drone Delivery to Your Balcony
Brunner, G., Szebedy, B., Tanner, S., & Wattenhofer, R. P. (2019) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2019.8798337 · Cited by 95
VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems
Bavle, H., de la Puente, P., How, J., & Campoy, P. (2020) · IEEE Access · doi:10.1109/access.2020.2983121 · Cited by 81
PLF-VINS: Real-Time Monocular Visual-Inertial SLAM With Point-Line Fusion and Parallel-Line Fusion
Lee, J., & Park, S.-Y. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3095518 · Cited by 85
Robocentric visual–inertial odometry
Huai, Z., & Huang, G. (2018) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1177/0278364919853361 · Cited by 185
GapFlyt: Active Vision Based Minimalist Structure-Less Gap Detection For Quadrotor Flight
Sanket, N. J., Singh, C. D., Ganguly, K., Fermuller, C. M., & Aloimonos, Y. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2843445 · Cited by 91
Survey on Localization Systems and Algorithms for Unmanned Systems
Yuan, S., Wang, H., & Xie, L. (2020) · Unmanned Systems · doi:10.1142/s230138502150014x · Cited by 91
GNSS-denied UAV indoor navigation with UWB incorporated visual inertial odometry
Lin, H., & Zhan, J.-R. (2022) · Measurement · doi:10.1016/j.measurement.2022.112256 · Cited by 89
Robust Legged Robot State Estimation Using Factor Graph Optimization
Wisth, D., Camurri, M., & Fallon, M. F. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2933768 · Cited by 87
Keyframe‐based thermal–inertial odometry
Khattak, S., Papachristos, C., & Alexis, K. (2019) · Journal of Field Robotics · doi:10.1002/rob.21932 · Cited by 87
Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery
Shamwell, E. J., Lindgren, K., Leung, S., & Nothwang, W. D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2909895 · Cited by 86
Keyframe-based Direct Thermal–Inertial Odometry
Khattak, S., Papachristos, C., & Alexis, K. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793927 · Cited by 81
Tran, N. X. B., Rosser, K., & Chahl, J. (2021) · Journal of Imaging · doi:10.3390/jimaging7100217 · Cited by 82
Oleynikova, H., Lanegger, C., Taylor, Z., Pantic, M., Millane, A., Siegwart, R., & Nieto, J. I. (2020) · Journal of Field Robotics · doi:10.1002/rob.21950 · Cited by 77
Self-citationSchmid, L., Delmerico, J., Schönberger, J. L., Nieto, J. I., Pollefeys, M., Siegwart, R., & Cadena, C. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811877 · Cited by 73
Self-citationAvoiding Dynamic Small Obstacles With Onboard Sensing and Computation on Aerial Robots
Kong, F., Xu, W. D., Cai, Y., & Zhang, F. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3101877 · Cited by 72
maplab 2.0 – A Modular and Multi-Modal Mapping Framework
Cramariuc, A., Bernreiter, L., Tschopp, F., Fehr, M., Reijgwart, V., Nieto, J. I., Siegwart, R., & Cadena, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3227865 · Cited by 71
Self-citationExploiting Symmetries to Design EKFs With Consistency Properties for Navigation and SLAM
Brossard, M., Barrau, A., & Bonnabel, S. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2882714 · Cited by 69
Wang, S., & Ahmad, N. S. (2024) · IEEE Access · doi:10.1109/access.2024.3519874 · Cited by 68
Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry Using Points and Lines
Zheng, F., Tsai, G., Zhang, Z., Liu, S., Chu, C.-C., & Hu, H. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594354 · Cited by 68
UAV/UGV Autonomous Cooperation: UAV assists UGV to climb a cliff by attaching a tether
Miki, T., Khrapchenkov, P., & Hori, K. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794265 · Cited by 67
A Survey on Visual Navigation and Positioning for Autonomous UUVs
Qin, J., Li, M., Li, D., Zhong, J., & Yang, K. (2022) · Remote Sensing · doi:10.3390/rs14153794 · Cited by 65
A Framework for UAV Navigation and Exploration in GPS-Denied Environments
Vanegas, F., Gaston, K. J., Roberts, J. M., & Gonzalez, F. (2019) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2019.8741612 · Cited by 65
Edge-Assisted Multi-Robot Visual-Inertial SLAM With Efficient Communication
Liu, X., Wen, S., Zhao, J., Qiu, T. Z., & Zhang, H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3376427 · Cited by 64
RD-VIO: Robust visual-inertial odometry for mobile augmented reality in dynamic environments
Li, J., Pan, X., Huang, G., Zhang, Z., Wang, N., Bao, H., & Zhang, G. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3353263 · Cited by 62
“ROVIO [4] is another filtering-based system that used photometric errors for visual observation.”
MethodTeng, S., Mueller, M. W., & Sreenath, K. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561313 · Cited by 60
On-Manifold Model Predictive Control for Trajectory Tracking on Robotic Systems
Lu, G., Xu, W. D., & Zhang, F. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3212397 · Cited by 57
ORB-TEDM: An RGB-D SLAM Approach Fusing ORB Triangulation Estimates and Depth Measurements
Yuan, J., Zhu, S., Tang, K., & Sun, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3154800 · Cited by 56
A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
Huang, B., Zhao, J., Luo, S., & Liu, J. (2021) · Journal of Global Positioning Systems · doi:10.5081/jgps.17.1.94 · Cited by 59
EqVIO: An Equivariant Filter for Visual-Inertial Odometry
van Goor, P., & Mahony, R. (2022) · IEEE Transactions on robotics · doi:10.1109/tro.2023.3289587 · Cited by 56
Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments
Khattak, S., Papachristos, C., & Alexis, K. (2019) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2019.8741787 · Cited by 52
Online multi-sensor calibration based on moving object tracking
Peršić, J., Petrović, L., Marković, I., & Petrovic, I. K. (2020) · Advanced Robotics · doi:10.1080/01691864.2020.1819874 · Cited by 51
Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review
Yang, M., Sun, X. S., Jia, F., Rushworth, A. G. A., Dong, X., Zhang, S., Fang, Z., Yang, G., & Liu, B. (2022) · Polymers · doi:10.3390/polym14102019 · Cited by 50
Overview of Multi-Robot Collaborative SLAM from the Perspective of Data Fusion
Chen, W., Wang, X., Gao, S., Shang, G., Zhou, C., Li, Z., Xu, C., & Hu, K. (2023) · Machines · doi:10.3390/machines11060653 · Cited by 51
Huai, J., Lin, Y., Zhuang, Y., Toth, C. K., & Chen, D. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3172474 · Cited by 51
Visual Localization by Learning Objects-Of-Interest Dense Match Regression
Weinzaepfel, P., Csurka, G., Cabon, Y., & Humenberger, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00578 · Cited by 48
An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM
Geneva, P., Maley, J., & Huang, G. P. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01238 · Cited by 47
Decoupled Right Invariant Error States for Consistent Visual-Inertial Navigation
Yang, Y., Chen, C., Lee, W., & Huang, G. P. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3140054 · Cited by 46
Thermal-Inertial Localization for Autonomous Navigation of Aerial Robots through Obscurants
Papachristos, C., Mascarich, F., & Alexis, K. (2018) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2018.8453447 · Cited by 46
InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry
Liu, C., Jiang, C., & Wang, H. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3243520 · Cited by 45
LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry
Nguyen, T., Cao, M., Yuan, S., Lyu, Y., Nguyen, T. H., & Xie, L. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560954 · Cited by 45
Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM
Lv, J.-J., Lang, X.-L., Xu, J., Wang, M.-M., Liu, Y., & Zuo, X. (2023) · IEEE/ASME transactions on mechatronics · doi:10.1109/tmech.2023.3241398 · Cited by 59
Xue, C., Huang, Y., Zhao, C. B., Li, X., Mihaylova, L. S., Li, Y., & Chambers, J. A. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3213787 · Cited by 44
Robust Thermal-Inertial Localization for Aerial Robots: A Case for Direct Methods
Khattak, S., Mascarich, F., Dang, T., Papachristos, C., & Alexis, K. (2019) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2019.8798279 · Cited by 43
Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry
Wisth, D., Camurri, M., & Fallon, M. F. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197214 · Cited by 42
DVIO: An Optimization-Based Tightly Coupled Direct Visual-Inertial Odometry
Jiang, J.-Q., Yuan, J., Zhang, X.-T., & Zhang, X.-B. (2021) · IEEE transactions on industrial electronics (1982. Print) · doi:10.1109/tie.2020.3036243 · Cited by 37
Kalman Filters on Differentiable ManifoldsPreprint
He, D., Xu, W. D., & Zhang, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03804 · Cited by 42
Shamwell, E. J., Leung, S., & Nothwang, W. D. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593573 · Cited by 41
Wang, F., Zou, Y., Zhang, C., Buzzatto, J., Liarokapis, M., Castillo, E. D. R., & Lim, J. B. P. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105139 · Cited by 37
Chen, H., Quan, F., Fang, L., & Zhang, S. (2019) · Sensors · doi:10.3390/s19194253 · Cited by 41
Doer, C., & Trommer, G. F. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636799 · Cited by 41
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
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry
Schubert, D., Demmel, N., von Stumberg, L., Usenko, V., & Cremers, D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968539 · Cited by 39
A Survey of Simultaneous Localization and Mapping.
Huang, B., Zhao, J., & Liu, J. (2019) · Cited by 38
Edge-Based Monocular Thermal-Inertial Odometry in Visually Degraded Environments
Wang, Y., Chen, H., Liu, Y., & Zhang, S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3246381 · Cited by 37
An End-to-End UAV Simulation Platform for Visual SLAM and Navigation
Chen, S., Zhou, W., Yang, A., Chen, H., Li, B., & Wen, C. (2022) · Aerospace · doi:10.3390/aerospace9020048 · Cited by 37
The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception
Gómez, J. P. R., Tapia, R., Paneque, J. L., Grau, P., Eguíluz, A. G., Dios, J. R. M., & Ollero, A. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3056348 · Cited by 36
Schmid, L., Reijgwart, V., Ott, L., Nieto, J. I., Siegwart, R., & Cadena, C. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3068954 · Cited by 35
Self-citationA 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
PLJ-SLAM: Monocular Visual SLAM With Points, Lines, and Junctions of Coplanar Lines
Ren, G., Cao, Z., Liu, X., Tan, M. H., & Yu, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3185122 · Cited by 34
RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments
Ram, K., Kharyal, C., Harithas, S. S., & Krishna, K. M. (2021) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros51168.2021.9636522 · Cited by 33
Continuous-Time State Estimation Methods in Robotics: A Survey
Talbot, W., Nubert, J., Tuna, T., Cadena, C., Dümbgen, F., Tordesillas, J., Barfoot, T. D., & Hutter, M. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3593079 · Cited by 32
Self-citationHe, D., Xu, W. D., & Zhang, F. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3237872 · Cited by 32
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
Design and Autonomous Stabilization of a Ballistically-Launched Multirotor
Bouman, A., Nadan, P. M., Anderson, M., Pastor, D., Izraelevitz, J., Burdick, J. W., & Kennedy, B. A. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197542 · Cited by 31
Tightly-Coupled Fusion of VINS and Motion Constraint for Autonomous Vehicle
Yu, Z., Zhu, L., & Lu, G. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3162155 · Cited by 31
A real time LiDAR-Visual-Inertial object level semantic SLAM for forest environments
Liu, H., Xu, G., Liu, B., Li, Y., Yang, S., Tang, J., Pan, K., & Xing, Y. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.11.013 · Cited by 33
Metadata
- Authors
- Bloesch, M., Burri, M., Omari, S., Hutter, M., & Siegwart, R.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Bloesch, Michael
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2017-09
- Volume
- 36
- Issue
- 10
- Pages
- 1053-1072
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2754177129
- Cited by
- 485
- References
- 37
- Citations by year
- 2026: 30 · 2025: 53 · 2024: 53 · 2023: 64 · 2022: 69 · 2021: 68 · 2020: 51 · 2019: 68 · 2018: 29
- FWCI
- 37.21
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- The International Journal of Robotics Research (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- The International Journal of Robotics Research
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (149427, 200021)
- National Centre of Competence in Research Robotics
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- visual-inertial odometry, iterated extended Kalman filter, photometric errors, data association, landmark initialization, motion blur robustness, pose estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
