为使机器人自主导航系统能够快速有效地提取前方路面，提出了一种可应用于结构化和半结构化环境的路面提取算法。该算法首先使用基于最大熵原理的模糊聚类方法，在单帧激光雷达数据中将具有连续趋势的点聚类；然后由聚类点拟合直线，再根据直线段的位置和角度的不同提取路面；最后通过比较连续几帧雷达数据来确定正常路面的参考水平面，以进一步提取路面上的障碍，为局部路径规划提供参考依据。在光照不均、纹理复杂、路边与路面高度差不确定、路边几何形状不规则的道路环境中的实验表明，该算法不仅能在结构化环境下提取路面，而且还可以在路边形状不规则的半结构环境下提取路面。

We propose a road-surface abstraction algorithm which is suitable for structured or semi-structured road environment and extracts effectively a road-surface for ground robot navigation. A fuzzy cluster method based on maximum entropy theories was employed to cluster Lidar points. After fitting clustered data linearly, the algorithm extracts seams that belong to road-surface by their location and angle. Current referenced horizontal can be acquired by comparing several continuous Lidar frames and then the algorithm extracts the obstacles in road area. Experiments show the algorithm works well in spite of road-boundary has regular shape or not, and is free from the impact of complex texture or irregular illumination of the road.