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Development of a Method for Data Dimensionality Reduction in Loop Closure Detection: An Incremental Approach

Published online by Cambridge University Press:  17 July 2020

Leandro A. S. Moreira Open the ORCID record for Leandro A. S. Moreira [Opens in a new window] ,
Claudia M. Justel ,
Jauvane C. de Oliveira  and
Paulo F. F. Rosa
Leandro A. S. Moreira*
Affiliation:
Laboratório Nacional de Computação Científica, Brazil. E-mail: jauvane@acm.org Instituto Militar de Engenharia, Brazil. E-mails: cjustel@ime.eb.br, rpaulo@ime.eb.br
Claudia M. Justel
Affiliation:
Instituto Militar de Engenharia, Brazil. E-mails: cjustel@ime.eb.br, rpaulo@ime.eb.br
Jauvane C. de Oliveira
Affiliation:
Laboratório Nacional de Computação Científica, Brazil. E-mail: jauvane@acm.org
Paulo F. F. Rosa
Affiliation:
Instituto Militar de Engenharia, Brazil. E-mails: cjustel@ime.eb.br, rpaulo@ime.eb.br
*
*Corresponding author. E-mail: leandromoreira75@gmail.com
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Summary

This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.

Keywords

SLAMLoop closure detectionIncremental dimensionality reductionMobile robotsRobot localization

Information

Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 557 - 571
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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