Fast stereo matching using image pyramid for lunar rover

Dense stereo matching has been widely used in lunar rover and it is still a challenging problem, and the main task is to calculate the disparity map given two rectified images of one scene. Most algorithms assume that a maximal possible disparity exists and search all disparities in the range from the minimum to this maximal disparity. In the case of large images and wide disparity search range this can be very computational cost. To solve these problems, we propose a novel hierarchical stereo matching that reconstructs the disparity map of the scene based on the pyramid image and more global matching (MGM) method. This strategy first generates an image pyramid from the original images. And then for the coarsest level images of the pyramid, the disparity map is computed based on the full disparity search range of the coarsest level images. The disparity map of the coarse image is then used as prior to restrict the disparity search space for finer layer matching. We conduct a number of experiments with lunar rover images to evaluate the performance of method, and the experimental results proved the total amount of calculation of the novel MGM method is only 10% of the previous method. And the speed of stereo matching is highly increased and is also more accurate on lunar scenes from the obtained dense disparity maps.