基于不确定性知识的实时道路场景理解

由于室外机器人的工作环境非常复杂，因此机器人的视觉导航必须具有足够的智能和鲁棒性，为此，提出了一种基于不确定性知识的实时道路理解算法，该算法通过不确定性知识推理来融合多种信息和知识，以满足在复杂道路环境下的鲁棒性要求，它即使在有强烈阴影、水迹等干扰下也能给出比较好的结果；通过图象边缘信息的提取可以得到精确的道路边界，以满足视觉导航的精确性要求；同时在算法设计时，兼顾了实时性要求；使得算法得以实时实现，该算法已在实际的机器人上进行了测试，并得到了很好的结果。

An Uncertain Knowledge Based Real Time Road Scene Understanding Algorithm

Because the environment of outdoor robot is very complicated, the visual navigation should be intelligent and robust enough. In order to improve the performance of computer vision navigation, a real time road scene understanding algorithm based on uncertain knowledge is presented in this paper. In this algorithm uncertain knowledge reasoning is applied to fuse the various image information and application knowledge, which make the algorithm can recognize the road scene robustly, even with the noise such as strong shadow and water on the road. Color information is used to obtain a coarse road region at first. And then image edge information is used to provide the precise road edge, which can meet the accuracy need of the visual navigation task. And this algorithm is well designed for real time application, which ensures that the visual navigation can make decisions in time. This algorithm has been applied on a real robot which has been tested in the real road environment for half a year. The experiment results are satisfying.