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Semantic classification of mobile robot locations through 2D laser scans

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Abstract

Previous learning-based methods that rely on 2D laser data to classify indoor robot locations into semantic classes were successful in distinguishing between rooms and corridors. However, the classification accuracy remained low for doorway locations. We propose a semantic place classification method that uses a rule-based doorway detection algorithm followed by a classification scheme that models training data through either K-means clustering or learning vector quantization. We conducted extensive experiments on the Freiburg 79 dataset and compared our method to previous semantic place classification algorithms. The doorway detection algorithm we propose significantly increases the classification accuracy for doorway locations as compared to the state-of-the-art performance. We applied our method, trained on the Freiburg 79 dataset, to Freiburg 52 and ESOGU datasets in order to demonstrate its generalization ability.

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Acknowledgements

This work is supported by Eskisehir Osmangazi University Scientific Research Project (Project No: 201415012(2013-253)).

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Eskisehir Osmangazi University, Eskisehir, Turkey

    Burak Kaleci, Çağrı Mete Şenler, Helin Dutağacı & Osman Parlaktuna

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  1. Burak Kaleci
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  2. Çağrı Mete Şenler
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  3. Helin Dutağacı
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  4. Osman Parlaktuna
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Correspondence to Burak Kaleci.

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Kaleci, B., Şenler, Ç.M., Dutağacı, H. et al. Semantic classification of mobile robot locations through 2D laser scans. Intel Serv Robotics 13, 63–85 (2020). https://doi.org/10.1007/s11370-019-00295-6

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  • DOI: https://doi.org/10.1007/s11370-019-00295-6

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