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A Distributed Approach for the Implementation of Geometric Reconstruction-Based Visual SLAM Systems

Published online by Cambridge University Press:  21 July 2020

Otacílio de Araújo Ramos Neto ,
Abel Cavalcante Lima Filho  and
Tiago P. Nascimento Open the ORCID record for Tiago P. Nascimento [Opens in a new window]
Otacílio de Araújo Ramos Neto
Affiliation:
Embedded and Distributed Computing Laboratory (LACED), Instituto Federal de Educação Ciência e Tecnologia da Paraíba, Guarabira, Brazil
Abel Cavalcante Lima Filho
Affiliation:
Department of Mechanical Engineering, Universidade Federal da Paraíba, João Pessoa, Brazil
Tiago P. Nascimento*
Affiliation:
Lab of Systems Engineering and Robotics (LaSER), Department of Computer Systems, Universidade Federal da Paraíba, João Pessoa, Brazil
*
*Corresponding author. E-mail: [email protected]
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Summary

Visual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, making it a non-real-time solution. The VSLAM systems that employ geometric reconstructions are based on the parallel processing paradigm developed in the Parallel Tracking and Mapping (PTAM) algorithm. This type of system was created for processors that have exactly two cores. The various SLAM methods based on the PTAM were also not designed to scale to all the cores of modern processors nor to function as a distributed system. Therefore, we propose a modification to the pipeline for the execution of well-known VSLAM systems so that they can be scaled to all available processors during execution, thereby increasing their performance in terms of processing time. We explain the principles behind this modification via a study of the threads in the SLAM systems based on PTAM. We validate our results with experiments describing the behavior of the original ORB-SLAM system and the modified version.

Keywords

Visual SLAMDistributed computingMobile robotics
Type
Articles
Information
Robotica , Volume 39 , Issue 5 , May 2021 , pp. 749 - 771
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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