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Terrain Classification for Enhancing Mobility of Outdoor Mobile Robot  

Kim, Ja-Young (충남대학교 메카트로닉스 공학과)
Lee, Jong-Hwa (충남대학교 메카트로닉스 공학과)
Lee, Ji-Hong (충남대학교 메카트로닉스 공학과)
Kweon, In-So (한국과학기술원 전자 전산학부)
Publication Information
The Journal of Korea Robotics Society / v.5, no.4, 2010 , pp. 339-348 More about this Journal
Abstract
One of the requirements for autonomous vehicles on off-road is to move stably in unstructured environments. Such capacity of autonomous vehicles is one of the most important abilities in consideration of mobility. So, many researchers use contact and/or non-contact methods to determine a terrain whether the vehicle can move on or not. In this paper we introduce an algorithm to classify terrains using visual information(one of the non-contacting methods). As a pre-processing, a contrast enhancement technique is introduced to improve classification of terrain. Also, for conducting classification algorithm, training images are grouped according to materials of the surface, and then Bayesian classification are applied to new images to determine membership to each group. In addition to the classification, we can build Traversability map specified by friction coefficients on which autonomous vehicles can decide to go or not. Experiments are made with Load-Cell to determine real friction coefficients of various terrains.
Keywords
Image Processing; Vision Information; Over-segmentation; Bayesian Classification; Friction Coefficient; Terrain Classification; Traversability Map;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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