中文摘要：

动态障碍物的存在导致产生不一致的环境地图,为此设计了一种新的拓扑地图创建方法。该方法首先通过概率方法过滤运动障碍物的干扰信息,获得环境可行区域信息,再将可行区域信息作为GNG算法的输入空间,通过学习与不断增加新的拓扑节点,创建一致的环境拓扑地图。该方法具有自学习、自适应等特点。通过仿真与物理实验验证了其可行性与有效性。

英文摘要：

Dynamic obstacles may result in inconsistent environment map.Aiming at this problem,a new building method of topological map is designed and realized for mobile robot.Firstly,the feasible region′s information is obtained via a probabilistic method to filtrate spurious measurements of dynamic obstacles.And then,with this information as the growing neural gas（GNG） algorithm′s inputs,a consistent topological map is built by learning and adding new node.This proposed method is self-learning and adaptive.Both the simulation and physical experiments verify the feasibility and effectiveness of the proposed method.