Using AdaBoost for place labeling and topological map building

Martinez Mozos, Oscar, Stachniss, Cyrill, Rottmann, Axel and Burgard, Wolfram (2007) Using AdaBoost for place labeling and topological map building. In: Robotics research: results of the 12th International Symposium ISRR. STAR Springer Tracts in Advanced Robotics, 28 . Springer, Germany, pp. 453-472. ISBN 9783540481102

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Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that the ability to learn such semantic categories from sensor data or in maps enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, exploration, or localization. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from vision and laser range data into a strong classifier. We furthermore present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for robust online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce a new approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with probabilistic labeling. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various environments.

Additional Information:Extended version of the previous paper that appeared in the proceedings of the ISRR conference for the Springer book.
Keywords:robotics, machine learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
Relation typeTarget identifier
ID Code:9567
Deposited On:22 May 2013 06:28

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