Efficient scene simulation for robust monte carlo localization using an RGB-D camera
Name
Leonard_Efficient scene.pdf
Size
1.76 MB
Format
Adobe PDF
Checksum (MD5)
d9d9195854551e7b0210a75d58bf12a9
Author(s)
Fallon, Maurice Francis
Johannsson, Hordur
Leonard, John Joseph
Date Issued
May 14, 2013
Journal
Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
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
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2012.6224951
