Paper Info
Title
Vision Sensor-Based Road Detection for Field Robot Navigation.
Abstract
Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA)-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF) framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art.
Year
DOI
Venue
2015
10.3390/s151129594
SENSORS
Keywords
Field
DocType
robot navigation,road detection,MPGA,GrowCut,conditional random field
Data mining,Population,Electronic engineering,Robustness (computer science),Artificial intelligence,Cluster analysis,Genetic algorithm,Vanishing point,Conditional random field,Computer vision,GrowCut algorithm,Engineering,Robot
Journal
Volume
Issue
ISSN
15
11.0
1424-8220
Citations 
PageRank 
References 
6
0.56
10
Authors
4
Name
Order
Citations
PageRank
Keyu Lu1164.09
Li Jian28531.63
Xiangjing An3112.48
Hangen He430723.86