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Categorization of Indoor Places Using the Kinect Sensor
Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
* Author to whom correspondence should be addressed.
Received: 14 March 2012; in revised form: 16 May 2012 / Accepted: 16 May 2012 / Published: 22 May 2012
Abstract: The categorization of places in indoor environments is an important capability for service robots working and interacting with humans. In this paper we present a method to categorize different areas in indoor environments using a mobile robot equipped with a Kinect camera. Our approach transforms depth and grey scale images taken at each place into histograms of local binary patterns (LBPs) whose dimensionality is further reduced following a uniform criterion. The histograms are then combined into a single feature vector which is categorized using a supervised method. In this work we compare the performance of support vector machines and random forests as supervised classifiers. Finally, we apply our technique to distinguish five different place categories: corridors, laboratories, offices, kitchens, and study rooms. Experimental results show that we can categorize these places with high accuracy using our approach.
Keywords: Kinect sensor; place categorization; service robots
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Cite This Article
MDPI and ACS Style
Mozos, O.M.; Mizutani, H.; Kurazume, R.; Hasegawa, T. Categorization of Indoor Places Using the Kinect Sensor. Sensors 2012, 12, 6695-6711.
Mozos OM, Mizutani H, Kurazume R, Hasegawa T. Categorization of Indoor Places Using the Kinect Sensor. Sensors. 2012; 12(5):6695-6711.
Mozos, Oscar Martinez; Mizutani, Hitoshi; Kurazume, Ryo; Hasegawa, Tsutomu. 2012. "Categorization of Indoor Places Using the Kinect Sensor." Sensors 12, no. 5: 6695-6711.