In intelligent vehicle field, occupancy grid maps are popular tools for representing the environment. Usually, occupancy grids, mapping the environment as a field of uniformly distributed binary/ternary variables, are generated by various kinds of sensors (e.g. lidar, radar, monocular/binocular vision system). In literature, most of proposed occupancy grid mapping methods create array-based fixed-resolution maps in either cartesian, polar, or column/disparity spaces. The problems of such maps are accuracy deficiency and prohibitive memory cost when trying to increase the resolution. This paper addresses these issues by presenting a novel variableresolution occupancy grid map based on quadtree structure from stereovision measurements. In the proposed method, a quadtree-based grid map with a settled resolution is calculated from a stereoscopic system at first. Then, the previously created map adapts its resolution to actual stereo measurements by merging or splitting nodes in the quadtree structure. The principal advantage of the proposed method is the ability to improve map's accuracy. Meanwhile, compared with some existing methods, the stereo-vision based occupancy grid mapping algorithm is improved. Experimental results with real datasets demonstrate that the accuracy of grid map created by our method is promoted from decimeter to centimeter.