Robust real-time visual odometry for dense RGB-D mapping
This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D vi...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/97552 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1826210191753871360 |
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| author | Whelan, Thomas Johannsson, Hordur Kaess, Michael McDonald, John Leonard, John Joseph |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Whelan, Thomas Johannsson, Hordur Kaess, Michael McDonald, John Leonard, John Joseph |
| author_sort | Whelan, Thomas |
| collection | MIT |
| description | This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features. |
| first_indexed | 2024-09-23T14:45:23Z |
| format | Article |
| id | mit-1721.1/97552 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:45:23Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/975522022-09-29T10:20:23Z Robust real-time visual odometry for dense RGB-D mapping Whelan, Thomas Johannsson, Hordur Kaess, Michael McDonald, John Leonard, John Joseph Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mechanical Engineering Johannsson, Hordur Kaess, Michael Leonard, John Joseph This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features. United States. Office of Naval Research (Grant N00014-10-1-0936) United States. Office of Naval Research (Grant N00014-11-1-0688) United States. Office of Naval Research (Grant N00014-12-10020) 2015-06-29T15:43:45Z 2015-06-29T15:43:45Z 2013-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-5643-5 978-1-4673-5641-1 1050-4729 http://hdl.handle.net/1721.1/97552 Whelan, Thomas, Hordur Johannsson, Michael Kaess, John J. Leonard, and John McDonald. “Robust Real-Time Visual Odometry for Dense RGB-D Mapping.” 2013 IEEE International Conference on Robotics and Automation (May 2013). https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1109/ICRA.2013.6631400 Proceedings of the 2013 IEEE International Conference on Robotics and Automation Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Whelan, Thomas Johannsson, Hordur Kaess, Michael McDonald, John Leonard, John Joseph Robust real-time visual odometry for dense RGB-D mapping |
| title | Robust real-time visual odometry for dense RGB-D mapping |
| title_full | Robust real-time visual odometry for dense RGB-D mapping |
| title_fullStr | Robust real-time visual odometry for dense RGB-D mapping |
| title_full_unstemmed | Robust real-time visual odometry for dense RGB-D mapping |
| title_short | Robust real-time visual odometry for dense RGB-D mapping |
| title_sort | robust real time visual odometry for dense rgb d mapping |
| url | http://hdl.handle.net/1721.1/97552 https://orcid.org/0000-0002-8863-6550 |
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