Semantic place labeling with mobile robots

TL;DRAbstract

Indoor environments can typically be divided into places with di?erent functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation ofthe environment, and to improve its capabilites. As an example, natural languageterms like corridor or room can be used to communicate the position of the robotin a more intuitive way. Other tasks, like exploration or localization, can also becarried out by the robot in a better way when semantic information is taken intoaccount.In this thesis, we present a method that enables a mobile robot to classify thedi?erent places of indoor environments into semantic classes, and then use this information to extend its representations of the environments. The main idea is toclassify the position of the robot based on the current observations taken by therobot. In this work, we use as main observations the scans obtained from a laserrange sensor. Each scan is r

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