Effective height-grid map building using inverse perspective image

This paper discusses an efficient method for generating height-grid maps, which are widely utilized in developing mobile robots and autonomous vehicles. The proposed method consists of four steps. First, inverse perspective images (IPI) are obtained from wide-angle cameras equipped with a fish-eye lens. Second, dense motion stereo is performed using IPI. Next, depth information is calculated and mapped onto the grid map. Lastly, a fusion of the grid maps is executed to enhance the quality of the map using a modified temporal median filter. The method can efficiently collect height and position information about objects relative to the road surface. The reason is that wide-angle images are converted to IPI to remove the perspective distortion of the road surface, and the depth is calculated using dense motion stereo from IPI. Moreover, the efficiency of the proposed fusion method is significantly higher than that of previous methods. The proposed method is very simple, but the quality that could be obtained is similar to the quality produced by the conventional technique, and the building speed showed dramatically fast performance of up to 200 fps in a single core CPU. In addition, we used a variety of test data and showed that the result verifies the validity of the proposed algorithm.