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LIDAR and stereo combination for traversability assessment of off-road robotic vehicles

Published online by Cambridge University Press:  15 June 2015

Giulio Reina ,
Annalisa Milella  and
Rainer Worst
Giulio Reina*
Affiliation:
Department of Engineering for Innovation, University of Salento, Via Arnesano, 73100 Lecce, Italy
Annalisa Milella
Affiliation:
Institute of Intelligent Systems for Automation, National Research Council, via G. Amendola 122 D/O, 70126, Bari, Italy. E-mail: milella@ba.issia.cnr.it
Rainer Worst
Affiliation:
Fraunhofer IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany. E-mail: rainer.worst@iais.fraunhofer.de
*
*Corresponding author. E-mail: giulio.reina@unisalento.it
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Summary

Reliable assessment of terrain traversability using multi-sensory input is a key issue for driving automation, particularly when the domain is unstructured or semi-structured, as in natural environments. In this paper, LIDAR-stereo combination is proposed to detect traversable ground in outdoor applications. The system integrates two self-learning classifiers, one based on LIDAR data and one based on stereo data, to detect the broad class of drivable ground. Each single-sensor classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the classifier automatically learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers are statistically combined in order to exploit their individual strengths and reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in rural environments, are presented to validate and assess the performance of this approach, showing its effectiveness and potential applicability to autonomous navigation in outdoor contexts.

Keywords

Robotic vehiclesNavigation systemsSensor combinationOnline learning strategyUnmanned ground vehicles
Type
Articles
Information
Robotica , Volume 34 , Issue 12 , December 2016 , pp. 2823 - 2841
Copyright
Copyright © Cambridge University Press 2015 

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