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Supervised semantic labeling of places using information extracted from sensor data
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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.