Real-time objects recognition and obstacles avoidance for mobile robot

The paper describes a mobile robot application based on monocular vision and laser that recognizes environment objects with capacity of avoiding obstacles in real time. Object recognition algorithm is based on mobile robot vision combining adaptive appearance matching and Kalman filter. The algorithm can adjust color matching threshold adaptively to reduce the influence of brightness variations in the scene. First, it carries out color modeling of environment objects in the YCrCb color space. Then it detects edge features in the image sequence by the Sobel algorithm, and thus to identify the outlines of targets. Finally mobile robot can recognize these objects with high empirical probability under some prior knowledge. Kalman filter is used as our prediction module to search objects in view window instead of the whole image sequence and to reduce calculation time. A virtual sub-targets based algorithm is also presented for real-time obstacles avoidance for mobile robot. Experimental results show that objects recognition algorithm can adapt to brightness variations and is simple, effective, easy to imply and operates with high efficiency. Mobile robot can also avoid obstacles smoothly in real time while navigating in the real scene.