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

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.