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.
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“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
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“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
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LSD-SLAM: Large-scale direct monocular SLAM
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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
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Research works printed but not linked: 1
- Bloesch M, 2016, arXiv
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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
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
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