Machine Learning for Visual Navigation of Unmanned Ground Vehicles release_xnsl2j5plvdhbftlva4tsyuzmm

by Artem A. Lenskiy, Jong-Soo Lee

Released as a article .

2016  

Abstract

The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This manuscript reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the manuscript.
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Type  article
Stage   submitted
Date   2016-04-08
Version   v1
Language   en ?
arXiv  1604.02485v1
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