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Localization of a Mobile Robot Using Ceiling Image with Identical Features

Authors :
Nak Yong Ko
Tae-Yong Kuc
Sung Woo Noh
Source :
Journal of Korean Institute of Intelligent Systems. 26:160-167
Publication Year :
2016
Publisher :
Korean Institute of Intelligent Systems, 2016.

Abstract

This paper reports a localization method of a mobile robot using ceiling image. The ceiling has landmarks which are not distinguishablefrom one another. The location of every landmark in a map is given a priori while correspondence is not given between a detected landmark and a landmark in the map. Only the initial pose of the robot relative to the landmarks is given. The method uses particle filter approach for localization. Along with estimating robot pose, the method also associates a landmark in the map to a landmark detected from the ceiling image. The method is tested in an indoor environment which has circular landmarks on the ceiling. The test verifies the feasibility of the method in an environment where range data to walls or to beacons are not available or severely corrupted with noise. This method is useful for localization in a warehouse where measurement by Laser range finder and range data to beacons of RF or ultrasonic signal have large uncertainty.Key Words : Mobile Robot, Pose Estimation, Ceiling Image, Undistinguishable Landmarks, Particle Filter, Correspondence.이 논문은 2015학년도 조선대학교 학술연구비의 지원을 받아 연구되었음(This study was supported by research fund from Chosun University, 2015)This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Details

ISSN :
19769172
Volume :
26
Database :
OpenAIRE
Journal :
Journal of Korean Institute of Intelligent Systems
Accession number :
edsair.doi...........72b0801d4dd4e8abb3992158c7b13439
Full Text :
https://doi.org/10.5391/jkiis.2016.26.2.160