Paper Info
Title
Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments.
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.
Year
DOI
Venue
2013
10.1109/IROS.2013.6697101
IROS
Keywords
Field
DocType
mobile robots,vegetation
Radar,Computer vision,Obstacle,Object detection,Vegetation,Computer science,Remote sensing,Man-portable radar,Artificial intelligence,Probabilistic logic,Robot,Mobile robot
Conference
ISSN
Citations 
PageRank 
2153-0858
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Juhana Ahtiainen141.17
Thierry Peynot210714.82
Jari Saarinen314613.82
Steven Scheding4254.01