Map segmentation for simultaneous localization and mapping in ruins
Map segmentation for simultaneous localization and mapping in ruins
复制标题
地图分割，用于废墟中的同步定位和测绘
DOI：
10.1080/01691864.2015.1093428
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发表时间：
2016-01
影响因子：
2
通讯作者：
Zhao Mingyang
中科院分区：
文献类型：
--
作者：
Wang Nan;Ma Shugen;Li Bin;Wang Minghui;Zhao Mingyang
随机地震灾害形成的内部废墟的环境特征分布是不可预测的。因此，现有的地图分割方法需要预先设置参数，不能直接使用。针对地图分割中先验知识不足的问题，在分层同时定位与映射(SLAM)算法的框架下，提出了一种基于谱聚类的地图分割方法。该方法利用环境划分解决了SLAM算法的增量复杂度问题。根据观测环境的相似性，建立了加权图。通过测量期望信息增益和位置冗余度来生成图中的节点。然后，基于最小归一化割准则，将图划分为地图分割的主观结果。该算法基于SLAM算法固有的稀疏性，不仅降低了计算成本，而且使信息损失最小，从而保证了算法的全局一致性。最后，通过仿真和实验验证了该算法的可行性和有效性。图形摘要
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. Graphical Abstract
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DOI：
10.1109/robot.2006.1641810
发表时间：
2006-05
期刊：
Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
影响因子：
--
作者：
J. Blanco;Javier González;Juan-Antonio Fernández-Madrigal
通讯作者：
J. Blanco;Javier González;Juan-Antonio Fernández-Madrigal
DOI：
10.1109/robot.2006.1641808
发表时间：
2006-05
期刊：
Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
影响因子：
--
作者：
Z. Zivkovic;B. Bakker;B. Kröse
通讯作者：
Z. Zivkovic;B. Bakker;B. Kröse
DOI：
10.3724/sp.j.1218.2013.00762
发表时间：
2013
期刊：
Robot
影响因子：
--
作者：
Nan Wang;Shugen Ma;Bin Li;Minghui Wang;Min Zhao
通讯作者：
Nan Wang;Shugen Ma;Bin Li;Minghui Wang;Min Zhao
影响因子：
7.8
作者：
Blanco, Jose-Luis;Fernandez-Madrigal, Juan-Antonio;Gonzalez, Javier
通讯作者：
Gonzalez, Javier
影响因子：
7.8
作者：
Estrada, C;Neira, J;Tardós, JD
通讯作者：
Tardós, JD