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A Data Association Algorithm for SLAM Based on Central Difference Joint Compatibility Criterion and Clustering

Published online by Cambridge University Press:  14 January 2021

Dan Liu Open the ORCID record for Dan Liu [Opens in a new window]
Dan Liu*
Affiliation:
College of Transportation, Shandong University of Science and Technology, No. 579, Qianwan gang Road, Huangdao Zone, Qingdao City, Shandong Province, 266590, China
*
*Corresponding author. E-mail: danaliu@yeah.net
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Summary

A data association algorithm for simultaneous localization and mapping (SLAM) based on central difference joint compatibility (CDJC) criterion and clustering is proposed to obtain the data association results. Firstly, CDJC criterion is designed to calculate joint Mahalanobis distance. Secondly, ordering points to identify the clustering structure is used to divide all observed features into several groups. Thirdly, CDJC branch and bound method is designed to be performed in each group. The results based on simulation data and benchmark dataset show that the proposed algorithm has low computational complexity and provide accurate association results for SLAM of mobile robot.

Keywords

Mobile robotSimultaneous localization and mappingData associationCentral difference joint compatibility criterionOPTICS
Type
Article
Information
Robotica , Volume 39 , Issue 9 , September 2021 , pp. 1674 - 1691
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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