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Detection and Classification of Gateways for the Acquisition of Structured Robot Maps

机译:用于获取结构化机器人地图的网关的检测和分类

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The automatic acquisition of structured object maps requires sophisticated perceptual mechanisms that enable the robot to recognize the objects that are to be stored in the robot map. This paper investigates a particular object recognition problem: the automatic detection and classification of gateways in office environments based on laser range data. We will propose, discuss, and empirically evaluate a sensor model for crossing gateways and different approaches to gateway classification including simple maximum classifiers and HMM-based classification of observation sequences.
机译:自动获取结构化对象图需要复杂的感知机制,使机器人能够识别要存储在机器人图中的对象。本文研究了一个特殊的对象识别问题:基于激光距离数据的办公环境中网关的自动检测和分类。我们将提出,讨论和凭经验评估跨网关的传感器模型和网关分类的不同方法,包括简单的最大分类器和基于HMM的观察序列分类。

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