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Cited by 917

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Robust visual inertial odometry using a direct EKF-based approach

Open access
Bloesch, Michael, Omari, Sammy, Hutter, Marco, Siegwart, Roland·2015·2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI 10.1109/iros.2015.7353389
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Abstract

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).

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Metadata

Authors
Bloesch, M., Omari, S., Hutter, M., & Siegwart, R.
Venue
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Published
2015-09
Pages
298-304
DOI
10.1109/iros.2015.7353389
OpenAlex
W4246614213
Cited by
917
References
22
FWCI
96.30
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Robust visual inertial odometry using a direct EKF-based approach. https://app.cochat.ai/papers/10.1109/iros.2015.7353389
The paper
Bloesch, M., Omari, S., Hutter, M., & Siegwart, R. (2015). Robust visual inertial odometry using a direct EKF-based approach. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2015.7353389

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.