Stereo vision-based subpixel level free space boundary detection using modified u-disparity and preview dynamic programming

As free space boundary defined as the boundary between the closest obstacles and a road surface provides information about drivable space and obstacles, it has been one of the most important research topics in the automotive field. This paper proposes a method enhancing the accuracy of u-disparity-based free space boundary estimation to the subpixel level. The conventional method detects peak series by exploiting dynamic programming in u-disparity made by accumulating disparity maps depending on the u coordinate. It assumes that the bigger accumulation value of the u-disparity of a position and the smaller vertical coordinate change between the position and its previous column, the higher is the possibility that the position belongs to peak series. However, as the conventional method considers only vertical coordinate change with respect to the previous column, it has a problem that even a straight line will be penalized if it is not seen to be horizontal. To address this problem, this paper proposes preview dynamic programming, which is modified to consider the slope change with the previous and next column. Additionally, to solve the problem that a slope smaller than 45 cannot be represented by integer coordinates, this paper proposes modified u-disparity, which uses average coordinates weighted by u-disparity values within a fixed size window. Through qualitative evaluations with an open dataset, it is confirmed that the proposed method significantly mitigates the quantization noise of free space boundary estimated by the conventional method.