Information-Driven Adaptive Structured-Light Scanners
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...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/111676 https://orcid.org/0000-0002-9334-1706 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0003-4844-3495 |
| _version_ | 1811070198984212480 |
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| author | Rosman, Guy Rus, Daniela L Fisher, John W |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Rosman, Guy Rus, Daniela L Fisher, John W |
| author_sort | Rosman, Guy |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:31:41Z |
| format | Article |
| id | mit-1721.1/111676 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:31:41Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1116762022-09-23T12:49:07Z Information-Driven Adaptive Structured-Light Scanners Rosman, Guy Rus, Daniela L Fisher, John W Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Rosman, Guy Rus, Daniela L Fisher, John W 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. United States. Office of Naval Research (Grant N00014-09-1-1051) United States. Army Research Office (Grant W911NF-11- 1-0391) United States. Office of Naval Research (Grant N00014- 11-1-0688) 2017-10-02T18:54:37Z 2017-10-02T18:54:37Z 2016-12 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-8851-1 1063-6919 http://hdl.handle.net/1721.1/111676 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) https://orcid.org/0000-0002-9334-1706 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0003-4844-3495 en_US http://dx.doi.org/10.1109/CVPR.2016.101 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT Web Domain |
| spellingShingle | Rosman, Guy Rus, Daniela L Fisher, John W Information-Driven Adaptive Structured-Light Scanners |
| title | Information-Driven Adaptive Structured-Light Scanners |
| title_full | Information-Driven Adaptive Structured-Light Scanners |
| title_fullStr | Information-Driven Adaptive Structured-Light Scanners |
| title_full_unstemmed | Information-Driven Adaptive Structured-Light Scanners |
| title_short | Information-Driven Adaptive Structured-Light Scanners |
| title_sort | information driven adaptive structured light scanners |
| url | http://hdl.handle.net/1721.1/111676 https://orcid.org/0000-0002-9334-1706 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0003-4844-3495 |
| work_keys_str_mv | AT rosmanguy informationdrivenadaptivestructuredlightscanners AT rusdanielal informationdrivenadaptivestructuredlightscanners AT fisherjohnw informationdrivenadaptivestructuredlightscanners |