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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: [email protected] Instituto Militar de Engenharia, Brazil. E-mails: [email protected], [email protected]
Claudia M. Justel
Affiliation:
Instituto Militar de Engenharia, Brazil. E-mails: [email protected], [email protected]
Jauvane C. de Oliveira
Affiliation:
Laboratório Nacional de Computação Científica, Brazil. E-mail: [email protected]
Paulo F. F. Rosa
Affiliation:
Instituto Militar de Engenharia, Brazil. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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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
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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