Vision Sensor-Based Road Detection for Field Robot Navigation

Sensors

Keyu Lu¹  Jian Li²  Xiangjing An²  Hangen He²  Lianqing Liu²  Ning Xi²  Wen Jung Li²  Xin Zhao² 

[1]College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, Hunan, China

关键词: robot navigation;    road detection;    MPGA;    GrowCut;    conditional random field;   

DOI  :  10.3390/s151129594

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.