Information-Driven Adaptive Structured-Light Scanners
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Rus_Information-driven.pdf
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2.81 MB
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Adobe PDF
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Author(s)
Rosman, Guy
Rus, Daniela L
Fisher, John W
Date Issued
December 2016
Journal
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Rosman, Guy et al. “Information-Driven Adaptive Structured-Light Scanners.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30 2016, Las Vegas, Nevada, USA, Institute of Electrical and Electronics Engineers (IEEE), December 2016: 874-883 © 2016 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CVPR.2016.101
