Efficient scene simulation for robust monte carlo localization using an RGB-D camera
This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2013
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| Online Access: | http://hdl.handle.net/1721.1/78893 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1811083213594951680 |
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| author | Fallon, Maurice Francis Johannsson, Hordur 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 Fallon, Maurice Francis Johannsson, Hordur Leonard, John Joseph |
| author_sort | Fallon, Maurice Francis |
| collection | MIT |
| description | This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing. |
| first_indexed | 2024-09-23T12:28:35Z |
| format | Article |
| id | mit-1721.1/78893 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:28:35Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/788932022-09-28T08:04:58Z Efficient scene simulation for robust monte carlo localization using an RGB-D camera Fallon, Maurice Francis Johannsson, Hordur Leonard, John Joseph Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mechanical Engineering Fallon, Maurice Francis Johannsson, Hordur Leonard, John Joseph This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing. 2013-05-14T19:54:50Z 2013-05-14T19:54:50Z 2013-05-14 2012-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-1404-6 978-1-4673-1403-9 1050-4729 http://hdl.handle.net/1721.1/78893 Fallon, Maurice F., Hordur Johannsson, and John J. Leonard. “Efficient scene simulation for robust monte carlo localization using an RGB-D camera.” Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA) (2012: 1663–1670. https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1109/ICRA.2012.6224951 Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Fallon, Maurice Francis Johannsson, Hordur Leonard, John Joseph Efficient scene simulation for robust monte carlo localization using an RGB-D camera |
| title | Efficient scene simulation for robust monte carlo localization using an RGB-D camera |
| title_full | Efficient scene simulation for robust monte carlo localization using an RGB-D camera |
| title_fullStr | Efficient scene simulation for robust monte carlo localization using an RGB-D camera |
| title_full_unstemmed | Efficient scene simulation for robust monte carlo localization using an RGB-D camera |
| title_short | Efficient scene simulation for robust monte carlo localization using an RGB-D camera |
| title_sort | efficient scene simulation for robust monte carlo localization using an rgb d camera |
| url | http://hdl.handle.net/1721.1/78893 https://orcid.org/0000-0002-8863-6550 |
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