Stereo Visual Inertial Odometry for Robots with Limited Computational Resources*

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Abstract

Current existing stereo visual odometry algorithms are computationally too expensive for robots with restricted resources. Executing these algorithms on such robots leads to a low frame rate and unacceptable decay in accuracy. We modify S-MSCKF, one of the most computationally efficient stereo Visual Inertial Odometry (VIO) algorithm, to improve its speed and accuracy when tracking low numbers of features. Specifically, we implement the Inverse Lucas-Kanade (ILK) algorithm for feature tracking and stereo matching. An outlier detector based on the average sum square difference of the template and matching warp in the ILK ensures higher robustness, e.g., in the presence of brightness changes. We restrict stereo matching to slide the window only in the x-direction to further decrease the computational costs. Moreover, we limit detection of new features to the regions of interest that have too few features. The modified S-MSCKF uses half of the processing time while obtaining competitive accuracy. This allows the algorithm to run in real-time on the extremely limited Raspberry Pi Zero single-board computer.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Subtitle of host publicationProceedings
PublisherIEEE
Pages9154-9159
Number of pages6
ISBN (Electronic)978-1-6654-1714-3
ISBN (Print)978-1-6654-1715-0
DOIs
Publication statusPublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Online at Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Country/TerritoryCzech Republic
CityOnline at Prague
Period27/09/211/10/21

Bibliographical note

This work was supported by Royal Brinkman
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Aerial Systems: Perception and Autonomy
  • Vision-Based Navigation
  • Computational Efficiency

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