Multi-modal Semantic Place Classification

The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a l...

• Type: Journal
• Year: 2010
• Where: IJRR
• Authors: Andrzej Pronobis, Óscar Martínez Mozos, Barbara Caputo, Patric Jensfelt
• Keywords: Cue Integration, IJRR 2010, Mobile Robot, Multi-modal Place Classification