基于层次化SLAM的未知环境级联地图创建方法

A Layered SLAM based Approach for Unknown Environment Hierarchical Map Building

针对大规模环境下障碍物、环境传感器、移动机器人自身位姿均未知的情况,提出层次化机器人同时定位与空间级联地图创建(SLAM)方法.首先建立子地图与拓扑节点两层结构环境模型,然后利用环境传感器感知信息辅助子地图局部坐标框架的在线创建和更新,同时在拓扑结构创建过程中,利用coupling summation公式推算节点间相对坐标关系.在机器人轨迹闭合检测的基础上,引入加权扫描匹配法和松弛法对拓扑结构进行优化,确保地图的全局一致性.实验验证了该方法的可行性与有效性.

A layered simultaneous localization and mapping（SLAM） approach for building spatial hierarchical maps is proposed in situations that obstacles,environmental sensors and robot poses are all unknown in large-scale environments. Firstly,a two-layered environmental model is established,which is composed of a submap layer and a topological node layer. Then the perception information of the environmental sensors is employed to assist the online creation and update of submaps local coordinate framework.While building the topological structure,the coupling summation equation is utilized to compute the relative positions between nodes.Based on the loop-closure detection result,the weighted scan matching algorithm and the relaxation algorithm are introduced to optimize the topological structure,which ensures global consistency of the map. Experimental results validate the feasibility and effectiveness of the approach.