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Proceedings Paper

Systematic and comparative analysis of techniques for SLAM development in mobile robotics
Author(s): Stalin Quimbita; Diego Chuquitarco; Vicente Hallo; Nancy Velasco; Dario Mendoza
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Paper Abstract

In recent decades, great researchers have focused on the development of simultaneous localization and mapping. Thus, SLAM technology (Simultaneous Localization and Mapping) has gone through a great technological evolution over the years. The present investigative work shows a chronology of milestones in the evolution of the SLAM of mobile robotics until today with a comparison of the main techniques that have been developed, aiming to identify the most suitable algorithm to perform a generation of 3D SLAM in real time in terms to improve accuracy, speed, and robustness among other aspects. These features will help to reduce mapping reconstructions errors, to improve the localization of a robot, and to provide greater reliability of the environment worked. This analysis will be established under some specific research criteria and as a prelude to a project focused on making a mobile robot capable of providing relevant information for search and rescue of people in critical situations, situations in which where it will be essential to perform SLAM 3D reliably and in real time. Thus, the present work it is focused to do an important systematic analysis regarding mobile robotics on the need to reason in a world that is sometimes confusing, sometimes dynamic and changing.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412X (15 March 2019); doi: 10.1117/12.2522937
Show Author Affiliations
Stalin Quimbita, Univ. de las Fuerzas Armadas ESPE (Ecuador)
Diego Chuquitarco, Univ. de las Fuerzas Armadas ESPE (Ecuador)
Vicente Hallo, Univ. de las Fuerzas Armadas ESPE (Ecuador)
Nancy Velasco, Univ. de las Fuerzas Armadas ESPE (Ecuador)
Dario Mendoza, Univ. de las Fuerzas Armadas ESPE (Ecuador)

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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