BibTeX
@MISC{Mingjun_experimentalvalidation,
author = {Wang Mingjun and Yi Xinhua and Wang Xuefeng},
title = {Experimental Validation for CRFNFP Algorithm},
year = {}
}
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Abstract
Abstract—In 2010,we proposed CRFNFP[1] algorithm to enhance long-range terrain perception for outdoor robots through the integration of both appearance features and spatial contexts. And our preliminary simulation results indicated the superiority of CRFNFP over other existing approaches in terms of accuracy, robustness and adaptability to dynamic unstructured outdoor environments. In this paper, we further study on the comparison experiments for navigation behaviors of robotic systems with different scene perception algorithms in real outdoor scenes. We implemented 3 robotic systems and repeated the running jobs under various conditions. We also defined 3 criterion to facilitate comparison for all systems: Obstacle Response Distance (ORD), Time to Finish Job (TFJ), and Distance of the Whole Run (DWR). The comparative experiments indicate that, the CRFNFP-based navigating system outperforms traditional localmap-based navigating systems in terms of all criterions. And the results also show that the CRFNFP algorithm does enhance the long-range perception for mobile robots and helps planning more efficient paths for the navigation. Keywords- autonomous nagivation; stereo vision; machine learning; conditional random fields; scene analysis. I.
Keyphrases
crfnfp algorithm experimental validation robotic system navigation behavior mobile robot obstacle response distance outdoor robot machine learning comparative experiment conditional random field unstructured outdoor environment real outdoor scene spatial context stereo vision long-range perception whole run comparison experiment crfnfp-based navigating system efficient path appearance feature preliminary simulation result different scene perception algorithm keywords autonomous nagivation various condition long-range terrain perception scene analysis
