A long range visual perception system is presented based on a multi-baseline stereo frame. The system is intended to be used onboard an autonomous vehicle operating in natural settings, such as an agricultural environment, to perform 3D scene reconstruction and segmentation tasks. First, the multi-baseline stereo sensor and the associated processing algorithms are described; then, a self-learning ground classifier is applied to segment the scene into ground and non-ground regions, using geometric features, without any a priori assumption on the terrain characteristics. Experimental results obtained with an off-road vehicle operating in an agricultural test field are presented to validate the proposed approach. It is shown that the use of a multi-baseline stereo frame allows for accurate reconstruction and scene segmentation at a wide range of viewing distances, thus increasing the overall flexibility and reliability of the perception system

A multi-baseline stereo system for scene segmentation in natural environments

Foglia M
2013

Abstract

A long range visual perception system is presented based on a multi-baseline stereo frame. The system is intended to be used onboard an autonomous vehicle operating in natural settings, such as an agricultural environment, to perform 3D scene reconstruction and segmentation tasks. First, the multi-baseline stereo sensor and the associated processing algorithms are described; then, a self-learning ground classifier is applied to segment the scene into ground and non-ground regions, using geometric features, without any a priori assumption on the terrain characteristics. Experimental results obtained with an off-road vehicle operating in an agricultural test field are presented to validate the proposed approach. It is shown that the use of a multi-baseline stereo frame allows for accurate reconstruction and scene segmentation at a wide range of viewing distances, thus increasing the overall flexibility and reliability of the perception system
2013 IEEE Conference on Technologies for Practical Robot Applications, TePRA 2013
978-1-4673-6223-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/22375
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