Object Detection Based on Plane Segmentation and Features Matching for a Service Robot

Main Authors: António J. R. Neves, Rui Garcia, Paulo Dias, Alina Trifan
Format: Article eJournal
Bahasa: eng
Terbitan: , 2016
Subjects:
Online Access: https://zenodo.org/record/1124017
ctrlnum 1124017
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Ant&#xF3;nio J. R. Neves</creator><creator>Rui Garcia</creator><creator>Paulo Dias</creator><creator>Alina Trifan</creator><date>2016-03-01</date><description>With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot.</description><identifier>https://zenodo.org/record/1124017</identifier><identifier>10.5281/zenodo.1124017</identifier><identifier>oai:zenodo.org:1124017</identifier><language>eng</language><relation>doi:10.5281/zenodo.1124016</relation><relation>url:https://zenodo.org/communities/waset</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Service Robot</subject><subject>Object Recognition</subject><subject>3D Sensors</subject><subject>Plane Segmentation.</subject><title>Object Detection Based on Plane Segmentation and Features Matching for a Service Robot</title><type>Journal:Article</type><type>Journal:Article</type><recordID>1124017</recordID></dc>
language eng
format Journal:Article
Journal
Journal:eJournal
author António J. R. Neves
Rui Garcia
Paulo Dias
Alina Trifan
title Object Detection Based on Plane Segmentation and Features Matching for a Service Robot
publishDate 2016
topic Service Robot
Object Recognition
3D Sensors
Plane Segmentation
url https://zenodo.org/record/1124017
contents With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot.
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