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Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing.

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Názov: Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing.
Autori: Romero-Ramirez, Francisco J.1 (AUTHOR) francisco.romero@urjc.es, Cazorla, Miguel2 (AUTHOR) miguel.cazorla@ua.es, Marín-Jiménez, Manuel J.3,4 (AUTHOR) mjmarin@uco.es, Medina-Carnicer, Rafael3,4 (AUTHOR) rmedina@uco.es, Muñoz-Salinas, Rafael3,4 (AUTHOR) rmsalinas@uco.es
Zdroj: Sensors (14248220). Jan2025, Vol. 25 Issue 2, p365. 20p.
Predmety: *DIGITAL maps, *PARALLEL processing, *SUPPLY & demand, *COMPARATIVE studies, *COMPUTERS
Abstrakt: Simultaneous Localization and Mapping (SLAM) systems face high computational demands, hindering their real-time implementation on low-end computers. An approach to addressing this challenge involves offline processing, i.e., a map of the environment map is created offline on a powerful computer and then passed to a low-end computer, which uses it for navigation, which involves fewer resources. However, even creating the map on a powerful computer is slow since SLAM is designed as a sequential process. This work proposes a parallel mapping method pSLAM for speeding up the offline creation of maps. In pSLAM, a video sequence is partitioned into multiple subsequences, with each processed independently, creating individual submaps. These submaps are subsequently merged to create a unified global map of the environment. Our experiments across a diverse range of scenarios demonstrate an increase in the processing speed of up to 6 times compared to that of the sequential approach while maintaining the same level of robustness. Furthermore, we conducted comparative analyses against state-of-the-art SLAM methods, namely UcoSLAM, OpenVSLAM, and ORB-SLAM3, with our method outperforming these across all of the scenarios evaluated. [ABSTRACT FROM AUTHOR]
Databáza: Academic Search Index
Popis
Abstrakt:Simultaneous Localization and Mapping (SLAM) systems face high computational demands, hindering their real-time implementation on low-end computers. An approach to addressing this challenge involves offline processing, i.e., a map of the environment map is created offline on a powerful computer and then passed to a low-end computer, which uses it for navigation, which involves fewer resources. However, even creating the map on a powerful computer is slow since SLAM is designed as a sequential process. This work proposes a parallel mapping method pSLAM for speeding up the offline creation of maps. In pSLAM, a video sequence is partitioned into multiple subsequences, with each processed independently, creating individual submaps. These submaps are subsequently merged to create a unified global map of the environment. Our experiments across a diverse range of scenarios demonstrate an increase in the processing speed of up to 6 times compared to that of the sequential approach while maintaining the same level of robustness. Furthermore, we conducted comparative analyses against state-of-the-art SLAM methods, namely UcoSLAM, OpenVSLAM, and ORB-SLAM3, with our method outperforming these across all of the scenarios evaluated. [ABSTRACT FROM AUTHOR]
ISSN:14248220
DOI:10.3390/s25020365