SVM-based image partitioning for vision recognition of AGV guide paths under complex illumination conditions. (February 2020)

• Record Type: Journal Article
• Authors: Wu, Xing; Sun, Chao; Zou, Ting; Li, Linhui; Wang, Longjun; Liu, Hui
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 61(2020)
• Publication Date: 2020-02
• Subjects: Vision guidance -- Path recognition -- Support vector machine -- Image processing -- Illumination-adaptive processing; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2019.101856
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: An illumination-adaptive path recognition approach is proposed to distinguish the original color features from their illumination artifacts. A path image is divided to low-, normal-, and high-illumination regions adaptively using an OVR-architecture SVM classifier. Color enhancement and chrominance difference are performed in abnormal-illumination regions to suppress the illumination interferences. A low-cost vision guidance system is developed for line-tracking guidance of AGVs working with complex illumination. Abstract: 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.