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Cited by 509Robotics and Sensor-Based LocalizationAdvanced Vision and ImagingInertial Sensor and Navigation

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Error modeling in stereo navigation

Matthies, L., Shafer, S.·1987·IEEE Journal on Robotics and Automation
DOI 10.1109/jra.1987.1087097
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Abstract

In stereo navigation, a mobile robot estimates its position by tracking landmarks with on-board cameras. Previous systems for stereo navigation have suffered from poor accuracy, in part because they relied on scalar models of measurement error in triangulation. Using three-dimensional (3D) Gaussian distributions to model triangulation error is shown to lead to much better performance. How to compute the error model from image correspondences, estimate robot motion between frames, and update the global positions of the robot and the landmarks over time are discussed. Simulations show that, compared to scalar error models, the 3D Gaussian reduces the variance in robot position estimates and better distinguishes rotational from translational motion. A short indoor run with real images supported these conclusions and computed the final robot position to within two percent of distance and one degree of orientation. These results illustrate the importance of error modeling in stereo vision for this and other applications.

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Research works printed but not linked: 26

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Metadata

Authors
Matthies, L., & Shafer, S.
Affiliations
Carnegie Mellon University (US)
ORCID
Matthies, L.
Venue
IEEE Journal on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1987-06
Volume
3
Issue
3
Pages
239-248
ISSN
0882-4967 (ISSN-L)
DOI
10.1109/jra.1987.1087097
OpenAlex
W2113560487
Cited by
509
References
0
Citations by year
2025: 3 · 2024: 9 · 2023: 3 · 2022: 9 · 2021: 11 · 2020: 9 · 2019: 14 · 2018: 15 · 2017: 19 · 2016: 22 · 2015: 17 · 2014: 18 · 2013: 24 · 2012: 17
FWCI
40.43
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Journal on Robotics and Automation (publishedVersion)
Funders
  • University of Waterloo
  • National Research Council Canada
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
Keywords
error modeling, mobile robot localization, landmark tracking, image correspondence, robot motion estimation, positioning

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