Abstract
In this paper we propose a novel approach for low cost multimodal detection of humans with mobile service robots. Detecting people is a key capability for robots that operate in populated environments. The main objective of this article is to illustrate the implementation of machine learning paradigms with computer vision techniques to improve human detection using 3D vision and a thermal sensor. Experimental results carried out in a manufacturing shop-floor show that the percentage of wrong classified using only Kinect is drastically reduced with the classification algorithms and with the combination of the three information sources.
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References
Heimann sensor, http://www.heimannsensor.com/index.php
Kinect sensor, http://en.wikipedia.org/wiki/Kinect
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Susperregi, L., Sierra, B., Martínez-Otzeta, J.M., Lazkano, E., Ansuategui, A. (2012). A Layered Learning Approach to 3D Multimodal People Detection Using Low-Cost Sensors in a Mobile Robot. In: Novais, P., Hallenborg, K., Tapia, D., Rodríguez, J. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent and Soft Computing, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28783-1_4
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DOI: https://doi.org/10.1007/978-3-642-28783-1_4
Publisher Name: Springer, Berlin, Heidelberg
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