震后建筑内部层次化SLAM的地图模型转换方法

A Model Transformation of Map Representation for Hierarchical SLAM That Can Be Used for After-earthquake Buildings

由于震后建筑内部环境受损程度和震害形态分布的无法预见性,层次化SLAM地图模型转换难以直接预设划分参数.本文提出一种基于图形分割的区域划分方法,实现层次化SLAM地图模型转换.通过对机器人里程和观测信息进行图形映射,基于信息熵生成节点集,将环境相似度作为边的权重,构建无向加权图及相似度矩阵;并采用归一化割策略对图形进行划分,得到以机器人观测视角的环境空间划分结果;方法在解决SLAM计算量递增问题的基础上,最小化相关信息损失,确保全局一致性.最后,通过仿真及模拟废墟实验,验证算法的有效性和可行性.

Because of the unpredictability of destructiveness and distribution of seismic damages in the after-earthquake buildings, it is difficult to preset an appropriate parameter of partition for a model transformation of map representation for hierarchical SLAM directly. In this paper, based on the graph partition, we propose a spatial segmentation method to achieve the model transformation of map representations for the hierarchical SLAM. The information of odometers and observations of the robot are abstracted as a graph. The generation of nodes is based on the information entropy and the weight of edges is measured by the environmental similarity. An auxiliary weighted graph and the similarity matrix are built. The normalized cut strategy is used to divide the graph and an environmental partition is obtained from the robot＇s point-of-view. On the premise of solving the problem of computational complexity increment, the loss of relevant information is minimized and the consistency of global mapping is ensured. Finally, the feasibility and validity of the oroDosed algorithm are verified by simulations and experiments in artificial ruins.