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Intelligent stereo vision in autonomous robot traversability estimation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2013 (English)In: Robotics: Concepts, Methodologies, Tools, and Applications, IGI Global, 2013, Vol. 1, 350-365 p.Chapter in book (Other academic)Text
Abstract [en]

Traversability estimation is the process of assessing whether a robot is able to move across a specific area. Autonomous robots need to have such an ability to automatically detect and avoid non-traversable areas and, thus, stereo vision is commonly used towards this end constituting a reliable solution under a variety of circumstances. This chapter discusses two different intelligent approaches to assess the traversability of the terrain in front of a stereo vision-equipped robot. First, an approach based on a fuzzy inference system is examined and then another approach is considered, which extracts geometrical descriptions of the scene depth distribution and uses a trained support vector machine (SVM) to assess the traversability. The two methods are presented and discussed in detail.

Place, publisher, year, edition, pages
IGI Global, 2013. Vol. 1, 350-365 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-181276DOI: 10.4018/978-1-4666-4607-0.ch017ScopusID: 2-s2.0-84944893744ISBN: 9781466646087OAI: diva2:902550

QC 20160211

Available from: 2016-02-11 Created: 2016-01-29 Last updated: 2016-02-11Bibliographically approved

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Nalpantidis, Lazaros
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