Visual localisation in outdoor industrial building environments

Nuske, S., Roberts, J. and Wyeth, G. F. (2008). Visual localisation in outdoor industrial building environments. In: Robotics and Automation 2008 (ICRA 2008). IEEE International Conference on Robotics and Automation 2008 (ICRA 2008), Pasadena, CA, U.S.A., (544-550). 19-23 May 2008. doi:10.1109/ROBOT.2008.4543263


Author Nuske, S.
Roberts, J.
Wyeth, G. F.
Title of paper Visual localisation in outdoor industrial building environments
Conference name IEEE International Conference on Robotics and Automation 2008 (ICRA 2008)
Conference location Pasadena, CA, U.S.A.
Conference dates 19-23 May 2008
Proceedings title Robotics and Automation 2008 (ICRA 2008)   Check publisher's open access policy
Journal name 2008 Ieee International Conference On Robotics and Automation, Vols 1-9   Check publisher's open access policy
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE - Institute of Electrical Electronics Engineers Inc.
Publication Year 2008
Sub-type Fully published paper
DOI 10.1109/ROBOT.2008.4543263
Open Access Status
ISBN 978-1-4244-1646-2
ISSN 1050-4729
Start page 544
End page 550
Total pages 7
Language eng
Abstract/Summary This paper presents a vision-based method of vehicle localisation that has been developed and tested on a large forklift type robotic vehicle which operates in a mainly outdoor industrial setting. The localiser uses a sparse 3D-edge- map of the environment and a particle filter to estimate the pose of the vehicle. The vehicle operates in dynamic and non-uniform outdoor lighting conditions, an issue that is addressed by using knowledge of the scene to intelligently adjust the camera exposure and hence improve the quality of the information in the image. Results from the industrial vehicle are shown and compared to another laser-based localiser which acts as a ground truth. An improved likelihood metric, using per- edge calculation, is presented and has shown to be 40% more accurate in estimating rotation. Visual localization results from the vehicle driving an arbitrary 1.5 km path during a bright sunny period show an average position error of 0.44 m and rotation error of 0.62deg.
Subjects E1
080101 Adaptive Agents and Intelligent Robotics
861699 Computer Hardware and Electronic Equipment not elsewhere classified
Keyword Forklift type robotic vehicle
Industrial vehicle
Pose estimation
Industrial robots
Robot vision
Q-Index Code E1
Q-Index Status Confirmed Code

 
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Created: Tue, 14 Apr 2009, 00:42:16 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering