Using AdaBoost for Place Labeling and Topological Map Building View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007-01-01

AUTHORS

Óscar Martínez Mozos , Cyrill Stachniss , Axel Rottmann , Wolfram Burgard

ABSTRACT

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. More... »

PAGES

453-472

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-48113-3_39

DOI

http://dx.doi.org/10.1007/978-3-540-48113-3_39

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002737466


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