In this paper, we propose a method to give more autonomy to a mobile robot by providing vision sensors. The proposed autonomous mobile robot consists of vision, decision, and moving systems. The vision system is based on the stereo technology, which needs correspondence between a set of identical points in the left and the right images. Although mean square difference (MSD) is generally used for the measure of correspondence, it is prone to various types of error caused by: shades, color change, and repetitive texture, to name a few. To correct this error, the fourdirection method, which incorporates surrounding information into correspondence measuring, can be used to improve the accuracy of correspondence. Edge of object is first extracted from the Laplacian of Gaussian (LoG) filtered image and post-treatment is performed to eliminate remaining high-frequency noise. During the process to minimize the change of edge, an adaptive threshold value is applied. The extracted edge image is then segmented based on histogram, and it precisely scans candidate blocks for accurate extraction of object. Even if the mobile robot is guaranteed to move autonomously, it has to sublate meaningless movement. To this end, the target is set up for the robot to be able to move toward the designated target and the robot is made to perceive the target by using structural information. The decision system utilizes three-dimensional (3D) distance information extracted from stereo vision and enables dynamic movement to look for a target. Experimental results show average error of 1.25% in the distance estimation, 97% recognition rate of target objects, and 2.3% collision rate with obstacles.