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Sensors 2012, 12(5), 6695-6711; doi:10.3390/s120506695
Article

Categorization of Indoor Places Using the Kinect Sensor

1,* , 2
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 and 1
Received: 14 March 2012; in revised form: 16 May 2012 / Accepted: 16 May 2012 / Published: 22 May 2012
(This article belongs to the Section Physical Sensors)
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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 Kinect sensor; place categorization; service robots
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Mozos OM, Mizutani H, Kurazume R, Hasegawa T. Categorization of Indoor Places Using the Kinect Sensor. Sensors. 2012; 12(5):6695-6711.

Chicago/Turabian Style

Mozos, Oscar Martinez; Mizutani, Hitoshi; Kurazume, Ryo; Hasegawa, Tsutomu. 2012. "Categorization of Indoor Places Using the Kinect Sensor." Sensors 12, no. 5: 6695-6711.



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