Categorization of Indoor Places Using the Kinect Sensor

Categorization of Indoor Places Using the Kinect Sensor

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 de...

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Main Authors: Mozos, Oscar Martinez, Mizutani, Hitoshi, Kurazume, Ryo, Hasegawa, Tsutomu
Format: Online
Language:English
Published: Molecular Diversity Preservation International (MDPI) 2012
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386764/
id pubmed-3386764
recordtype oai_dc
spelling pubmed-33867642012-07-09 Categorization of Indoor Places Using the Kinect Sensor Mozos, Oscar Martinez Mizutani, Hitoshi Kurazume, Ryo Hasegawa, Tsutomu Article 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. Molecular Diversity Preservation International (MDPI) 2012-05-22 /pmc/articles/PMC3386764/ /pubmed/22778665 http://dx.doi.org/10.3390/s120506695 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Mozos, Oscar Martinez
Mizutani, Hitoshi
Kurazume, Ryo
Hasegawa, Tsutomu
spellingShingle Mozos, Oscar Martinez
Mizutani, Hitoshi
Kurazume, Ryo
Hasegawa, Tsutomu
Categorization of Indoor Places Using the Kinect Sensor
author_facet Mozos, Oscar Martinez
Mizutani, Hitoshi
Kurazume, Ryo
Hasegawa, Tsutomu
author_sort Mozos, Oscar Martinez
title Categorization of Indoor Places Using the Kinect Sensor
title_short Categorization of Indoor Places Using the Kinect Sensor
title_full Categorization of Indoor Places Using the Kinect Sensor
title_fullStr Categorization of Indoor Places Using the Kinect Sensor
title_full_unstemmed Categorization of Indoor Places Using the Kinect Sensor
title_sort categorization of indoor places using the kinect sensor
description 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.
publisher Molecular Diversity Preservation International (MDPI)
publishDate 2012
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386764/
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