Map segmentation for simultaneous localization and mapping in ruins.

The distribution of environmental features of the internal ruins which are formed by a randomly seismic disaster is unpredictable. Therefore, the existing methods of map segmentation, which need to preset parameters, cannot be directly used. Considering the lack of prior knowledge, a map segmentation method based on the spectral clustering is proposed in the framework of hierarchical simultaneous localization and mapping (SLAM) algorithm. The method solves the problem of incremental complexity of SLAM algorithm using the division of environment. In accordance with the similarity of observed environment, a weighted graph is established. The nodes in the graph are generated by measuring the expected information gain and position redundancy. Then, the graph is partitioned into subjective results of map segment based on the criterion of minimum normalized cut. On the basis of the inherent sparse of SLAM, the proposed algorithm not only reduces the cost of calculation, but also minimizes the loss of information in order to ensure the global consistency. Finally, the feasibility and effectiveness of the algorithm are verified by simulation and experiment. [ABSTRACT FROM PUBLISHER]