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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2016 - 3

A Novel Keep Zero as Zero Polar Correlation Technique for Mobile Robot Localization using LIDAR

SIDHARTHAN, R. K. See more information about SIDHARTHAN, R. K. on SCOPUS See more information about SIDHARTHAN, R. K. on IEEExplore See more information about SIDHARTHAN, R. K. on Web of Science, KANNAN, R. See more information about  KANNAN, R. on SCOPUS See more information about  KANNAN, R. on SCOPUS See more information about KANNAN, R. on Web of Science, SRINIVASAN, S. See more information about  SRINIVASAN, S. on SCOPUS See more information about  SRINIVASAN, S. on SCOPUS See more information about SRINIVASAN, S. on Web of Science, BALAS, M. M. See more information about BALAS, M. M. on SCOPUS See more information about BALAS, M. M. on SCOPUS See more information about BALAS, M. M. on Web of Science
 
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Download PDF pdficon (1,396 KB) | Citation | Downloads: 1,023 | Views: 3,180

Author keywords
correlation, mobile robots, pattern matching, sensor fusion, simultaneous localization and mapping

References keywords
systems(9), slam(9), scan(8), robotics(8), matching(8), fast(6), data(6), localization(5), laser(5), automation(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-11-30
Volume 16, Issue 4, Year 2016, On page(s): 15 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.04003
Web of Science Accession Number: 000390675900003
SCOPUS ID: 85007524779

Abstract
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Sensor fusion based localization techniques often need accurate estimate of the fast and uncertain scene change in environment. To determine the scene change from two consecutive LIDAR scans, this paper proposes a novel technique called 'keep zero as zero' polar correlation. As it name implies any zero in the scan data is kept isolated from scene change estimation as it do not carry any information about scene change. Unlike existing techniques, the proposed methodology employs minimization of selective horizontal and vertically shifted sum of difference between the scans to estimate scene change in terms of rotation and translation. Minimization of the proposed correlation function across the specified search space can guarantee an accurate estimate of scene change without any ambiguity. The performance of the proposed method is tested experimentally on a mobile robot in two modes depending on the scene change. In the first mode, scene change is detected using dynamic LIDAR, whereas static LIDAR is used in the second mode. The proposed methodology is found to be more robust to environmental uncertainties with a reliable level of localization accuracy.


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Faculty of Electrical Engineering and Computer Science
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