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Proceedings Paper

Mobile robots traversability awareness based on terrain visual sensory data fusion
Author(s): Amir Shirkhodaie
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Paper Abstract

In this paper, we have presented methods that significantly improve the robot awareness of its terrain traversability conditions. The terrain traversability awareness is achieved by association of terrain image appearances from different poses and fusion of extracted information from multimodality imaging and range sensor data for localization and clustering environment landmarks. Initially, we describe methods for extraction of salient features of the terrain for the purpose of landmarks registration from two or more images taken from different via points along the trajectory path of the robot. The method of image registration is applied as a means of overlaying (two or more) of the same terrain scene at different viewpoints. The registration geometrically aligns salient landmarks of two images (the reference and sensed images). A Similarity matching techniques is proposed for matching the terrain salient landmarks. Secondly, we present three terrain classifier models based on rule-based, supervised neural network, and fuzzy logic for classification of terrain condition under uncertainty and mapping the robot's terrain perception to apt traversability measures. This paper addresses the technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain spatial and textural cues.

Paper Details

Date Published: 2 May 2007
PDF: 12 pages
Proc. SPIE 6561, Unmanned Systems Technology IX, 65611U (2 May 2007); doi: 10.1117/12.718748
Show Author Affiliations
Amir Shirkhodaie, Tennessee State Univ. (United States)

Published in SPIE Proceedings Vol. 6561:
Unmanned Systems Technology IX
Grant R. Gerhart; Douglas W. Gage; Charles M. Shoemaker, Editor(s)

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