Optimizing Fiducial Marker Placement for Improved Visual Localization
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2211.01513.pdf
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11.6 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Huang, Qiangqiang
DeGol, Joseph
Fragoso, Victor
Sinha, Sudipta N.
Leonard, John J.
Date Issued
May 2023
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Q. Huang, J. DeGol, V. Fragoso, S. N. Sinha, and J. J. Leonard, “Optimizing Fiducial Marker Placement for Improved Visual Localization”, IEEE Robotics and Automation Letters (RA-L), 2023.
Version
Author's final manuscript
Abstract
Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This paper explores the problem of automatic marker placement within a scene. Specifically, given a predetermined set of markers and a scene model, we compute optimized marker positions within the scene that can improve accuracy in visual localization. Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene. We present optimized marker placement (OMP), a greedy algorithm that is based on the camera localizability framework. We have also designed a simulation framework for testing marker placement algorithms on 3D models and images generated from synthetic scenes. We have evaluated OMP within this testbed and demonstrate an improvement in the localization rate by up to 20 percent on four different scenes.
Subjects
Artificial Intelligence
Control and Optimization
Computer Science Applications
Computer Vision and Pattern Recognition
Mechanical Engineering
Human-Computer Interaction
Biomedical Engineering
Control and Systems Engineering
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/lra.2023.3260700
