Towards Semantic Navigation in Mobile Robotics
Towards Semantic Navigation in Mobile Robotics
复制标题
移动机器人中的语义导航
DOI：
10.1007/978-3-642-17322-6_30
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
J. Szklarski
中科院分区：
文献类型：
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作者：
A. Borkowski;B. Siemiątkowska;J. Szklarski
如今，移动的机器人在生产、公共交通、安全和国防、太空探索等许多领域都有应用。为了在这一工程领域取得进一步的进展，必须打破一个重要的障碍：机器人必须能够理解周围世界的意义。到目前为止，移动的机器人只能感知环境的几何特征。传感设备（摄像机，激光测距仪，微波雷达）和足够的计算能力的快速发展，使得有可能开发机器人控制器，拥有一定的知识的应用领域，并能够在语义层面上的原因。
论文的第一部分涉及专门在建筑物内操作的移动的机器人。提出了基于超图的语义导航概念。然后，它示出了如何语义信息，有用的移动的机器人，可以从建筑物的数字文档中提取。
在本文的第二部分中，我们报告了从激光扫描仪提供的原始数据中提取语义特征的最新结果。本研究的目的是开发一个系统，将使移动的机器人在建筑物中操作的能力，以识别和识别某些类的对象。该系统所涉及的数据处理技术包括在线更新的环境的3D模型、基于规则和基于特征的对象分类器、利用蜂窝网络的路径规划器和其他先进工具。在实际条件下进行的实验验证了所提出的解决方案。
Nowadays mobile robots find application in many areas of production, public transport, security and defense, exploration of space, etc. In order to make further progress in this domain of engineering, a significant barrier has to be broken: robots must be able to understand the meaning of surrounding world. Until now, mobile robots have only perceived geometrical features of the environment. Rapid progress in sensory devices (video cameras, laser range finders, microwave radars) and sufficient computational power available on-board makes it possible to develop robot controllers that possess certain knowledge about the area of application and which are able to reason at a semantic level.
The first part of the paper deals with mobile robots dedicated to operate inside buildings. A concept of the semantic navigation based upon hypergraphs is introduced. Then it is shown how semantic information, useful for mobile robots, can be extracted from the digital documentation of a building.
In the second part of the paper we report the latest results on extracting semantic features from the raw data supplied by laser scanners. The aim of this research is to develop a system that will enable a mobile robot to operate in a building with ability to recognise and identify objects of certain classes. Data processing techniques involved in this system include a 3D-model of the environment updated on-line, rule-based and feature-based classifiers of objects, a path planner utilizing cellular networks and other advanced tools. Experiments carried out under real-life conditions validate the proposed solutions.