SVM-based image partitioning for vision recognition of AGV guide paths under complex illumination conditions

被引:17
作者
Wu, Xing [1 ]
Sun, Chao [1 ]
Zou, Ting [2 ]
Li, Linhui [1 ]
Wang, Longjun [1 ]
Liu, Hui [3 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Jiangsu, Peoples R China
[2] Mem Univ Newfoundland, Dept Mech Engn, St John, NF, Canada
[3] Cent South Univ, Inst Artificial Intelligence & Robot, Changsha, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Vision guidance; Path recognition; Support vector machine; Image processing; Illumination-adaptive processing; OBJECT RECOGNITION; SYSTEM; LOCALIZATION; ROBUST; SLAM; CNN;
D O I
10.1016/j.rcim.2019.101856
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Applying computer vision to mobile robot navigation has been studied more than two decades. For the commercial off-the-shelf (COTS) automated guided vehicles (AGV) products, the cameras are still not widely used for the acquisition of guidance information from the environment. One of the most challenging problems for a vision guidance system of AGVs lies in the complex illumination conditions. Compared to the applications of computer vision where on-machine cameras are fixed in place, it is difficult to structure the illumination circumstance for an AGV that needs to travel through a large work space. In order to distinguish the original color features of path images from their illumination artifacts, an illumination-adaptive image partitioning approach is proposed based on the support vector machine (SVM) classifier with the slack constraint and the kernel function, which is utilized to divide a path image to low-, normal-, and high-illumination regions automatically. Moreover, an intelligent path recognition method is developed to carry out guide color enhancement and adaptive threshold segmentation in different regions. Experimental results show that the SVM-based classifier has the satisfactory generalization ability, and the illumination-adaptive path recognition approach has the high adaptability to the complex illumination conditions, when recognizing the path pixels in the field of view with both high-reflective and dark-shadow regions. The 98% average rate of path recognition will significantly facilitate the subsequent operation of path fitting for vision guidance of AGVs.
引用
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页数:14
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