Title
Stereo Vision Based Obstacle Avoidance Path-Planning for Cross-Country Intelligent Vehicle
Abstract
Intelligent vehicle navigation in complex cross-country terrain can benefit from accurate obstacle avoidance path-planning. The paper presents an obstacle avoidance path-planning algorithm based on stereo vision system. Firstly, Multi-feature extraction based stereo matching techniques is proposed on a corner and edge extracted image pair to accomplish environmental perception, which can provide robust detection results under complex conditions. Secondly, in order to plan a path for a vehicle, a method of analyzing three-dimensional data produced by stereo vision is described. It computes estimates of interpolated height, slope, and roughness at equally spaced grids, as well as accuracy estimates of each grid. Then fuzzy rule is designed to integrate slope, roughness and certain value to analysis the traversability. To realize obstacle avoidance path-planning for cross-country intelligent vehicle, traversability is considered in Vector Field Histogram (VFH) algorithm and obstacle avoidance behaviors are designed. Experiments confirm the effectiveness of the obstacle avoidance path-planning method.
Year
DOI
Venue
2009
10.1109/FSKD.2009.170
FSKD (5)
Keywords
Field
DocType
obstacle avoidance behavior,complex crosscountry terrain,cross-country intelligent vehicle,intelligent vehicle navigation,accurate obstacle avoidance path-planning,complex condition,obstacle avoidance path-planning,obstacle avoidance,stereo vision,complex cross-country terrain,stereo vision system,stereo matching technique,vector field histogram,edge detection,histograms,data mining,three dimensional,visual perception,fuzzy set theory,lighting,path planning,data analysis,feature extraction
Obstacle avoidance,Motion planning,Vector Field Histogram,Histogram,Computer vision,Computer science,Edge detection,Stereopsis,Artificial intelligence,Grid,Machine learning,Fuzzy rule
Conference
Citations 
PageRank 
References 
6
0.61
9
Authors
4
Name
Order
Citations
PageRank
Li Linhui160.61
Zhang Mingheng260.61
Lie Guo3565.25
Zhao Yibing460.61