Topological mapping using spectral clustering and classification
Topological mapping using spectral clustering and classification
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DOI：
10.1109/iros.2007.4399611
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
E. Brunskill;T. Kollar;N. Roy
中科院分区：
文献类型：
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作者：
E. Brunskill;T. Kollar;N. Roy
在这项工作中，我们提出了一个在线的方法，从原始传感器信息生成拓扑图。我们首先描述了一个算法，自动分解成子地图段使用图分区技术称为谱聚类。然后，我们描述了如何训练一个分类器，使用AdaBoost机器学习算法从激光签名中识别图形子图。我们证明，我们可以执行拓扑映射的机器人通过其环境中移动时，通过增量分割的世界，我们可以关闭循环时，学习分类器认识到，机器人已经返回到以前访问过的位置。
In this work we present an online method for generating topological maps from raw sensor information. We first describe an algorithm to automatically decompose a map into submap segments using a graph partitioning technique known as spectral clustering. We then describe how to train a classifier to recognize graph submaps from laser signatures using the AdaBoost machine learning algorithm. We demonstrate that the we can perform topological mapping by incrementally segmenting the world as the robot moves through its environment, and we can close the loop when the learned classifier recognizes that the robot has returned to a previously visited location.