Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments

Ahtiainen, Juhana, Peynot, Thierry, Saarinen, Jari, & Scheding, Steven (2013) Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments. In Proceeding of the 2013 IEEE/RSJ International Conference on Robots and Intelligent Systems, IEEE, Tokyo Big Sight, Tokyo, pp. 5148-5155.

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Abstract

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

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ID Code: 67656
Item Type: Conference Paper
Refereed: Yes
Additional Information: 14002410
ultrawideband radar
obstacle detection
vegetated environments
autonomous robots
dense vegetation
probabilistic traversability map
laser data
adaptive detection threshold
probabilistic sensor model
laser map
obstacle free foliage
Keywords: obstacle detection, mobile robots, traversability analysis, ultra wideband radar, vegetation
DOI: 10.1109/IROS.2013.6697101
ISSN: 2153-0858
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Deposited On: 06 Mar 2014 00:37
Last Modified: 07 Mar 2014 12:26

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