Supervised Learning of Places from Range Data using AdaBoost

— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of place improves the capabilities of a mobile robot in various domains including localization, path-planning, or human-robot interaction. Our approach uses AdaBoost, a supervised learning algorithm, to train a set of classifiers for place recognition based on laser range data. In this paper we describe how this approach can be applied to distinguish between rooms, corridors, doorways, and hallways. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various environments. I. I In the past, many researchers have considered the problem of building accurate metric or topological maps of the environment from the data gathered with a mobile robot. The question of how to augment such maps by semantic information, however, is ...

• Type: Conference
• Year: 2005
• Where: ICRA
• Authors: Óscar Martínez Mozos, Cyrill Stachniss, Wolfram Burgard
• Keywords: ICRA 2005, Mobile Robot, Robotics, Semantic Categories, Semantic Information