Multi-modal semantic place classification

Pronobis, Andrzej, Mozos, Oscar M., Caputo, Barbara and Jensfelt, Patric (2010) Multi-modal semantic place classification. International Journal of Robotics Research (IJRR), 29 (2-3). pp. 298-320. ISSN 0278-3649

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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 live experiment on an autonomous robot. The results clearly demonstrate the effectiveness of our cue integration scheme and its value for robust place classification under varying conditions.

Additional Information:Published online before print December 4, 2009
Keywords:recognition, sensor fusion, localization, multi-modal place classification, sensor and cue integration, semantic annotation of space
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G440 Human-computer Interaction
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:9322
Deposited On:07 May 2013 13:08

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