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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

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


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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  2/2016 - 4

Stochastic Wheel-Slip Compensation Based Robot Localization and Mapping

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, V. E. See more information about BALAS, V. E. on SCOPUS See more information about BALAS, V. E. on SCOPUS See more information about BALAS, V. E. on Web of Science
 
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Download PDF pdficon (853 KB) | Citation | Downloads: 369 | Views: 788

Author keywords
error compensation, Gaussian processes, mobile robots, motion estimation, simultaneous localization and mapping

References keywords
systems(9), robot(8), robots(7), mobile(7), system(6), vehicle(5), localization(5), intelligent(5), compensation(5), slip(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-05-31
Volume 16, Issue 2, Year 2016, On page(s): 25 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.02004
Web of Science Accession Number: 000376996100004
SCOPUS ID: 84974839110

Abstract
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Full text preview
Wheel slip compensation is vital for building accurate and reliable dead reckoning based robot localization and mapping algorithms. This investigation presents stochastic slip compensation scheme for robot localization and mapping. Main idea of the slip compensation technique is to use wheel-slip data obtained from experiments to model the variations in slip velocity as Gaussian distributions. This leads to a family of models that are switched depending on the input command. To obtain the wheel-slip measurements, experiments are conducted on a wheeled mobile robot and the measurements thus obtained are used to build the Gaussian models. Then the localization and mapping algorithm is tested on an experimental terrain and a new metric called the map spread factor is used to evaluate the ability of the slip compensation technique. Our results clearly indicate that the proposed methodology improves the accuracy by 72.55% for rotation and 66.67% for translation motion as against an uncompensated mapping system. The proposed compensation technique eliminates the need for extro receptive sensors for slip compensation, complex feature extraction and association algorithms. As a result, we obtain a simple slip compensation scheme for localization and mapping.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 271 TCR
SCOPUS® Citations for all references: 396 TCR

Web of Science® Average Citations per reference: 10 ACR
SCOPUS® Average Citations per reference: 15 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-09-17 10:06 in 205 seconds.




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