Cited by 485Robotics and Sensor-Based LocalizationInertial Sensor and NavigationAdvanced Vision and Imaging

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Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback

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Bloesch, Michael, Burri, Michael, Omari, Sammy, Hutter, Marco, Siegwart, Roland·2017·The International Journal of Robotics Research

DOI 10.1177/0278364917728574

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Abstract

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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Authors: Bloesch, M., Burri, M., Omari, S., Hutter, M., & Siegwart, R.
Affiliations: ETH Zurich (CH)
Corresponding author: Bloesch, Michael
ORCID: Bloesch, Michael
Burri, Michael
Hutter, Marco
Siegwart, Roland
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
DOI: 10.1177/0278364917728574
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
Licence: other-oa
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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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Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback

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