Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing
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...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/365 |
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| author | Francisco J. Romero-Ramirez Miguel Cazorla Manuel J. Marín-Jiménez Rafael Medina-Carnicer Rafael Muñoz-Salinas |
| author_facet | Francisco J. Romero-Ramirez Miguel Cazorla Manuel J. Marín-Jiménez Rafael Medina-Carnicer Rafael Muñoz-Salinas |
| author_sort | Francisco J. Romero-Ramirez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa2ed613918545959d449de56b258c30 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-aa2ed613918545959d449de56b258c302025-01-24T13:48:39ZengMDPI AGSensors1424-82202025-01-0125236510.3390/s25020365Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent ProcessingFrancisco J. Romero-Ramirez0Miguel Cazorla1Manuel J. Marín-Jiménez2Rafael Medina-Carnicer3Rafael Muñoz-Salinas4Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Campus de Fuenlabrada, Universidad Rey Juan Carlos, 28942 Fuenlabrada, SpainDepartamento de Ciencia de la Computación e Inteligencia Artificial, Carretera San Vicente del Raspeig s/n, Universidad de Alicante, 03690 San Vicente del Raspeig, SpainDepartamento de Informática y Análisis Numérico, Edificio Einstein, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainDepartamento de Informática y Análisis Numérico, Edificio Einstein, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainDepartamento de Informática y Análisis Numérico, Edificio Einstein, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainSimultaneous 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.https://www.mdpi.com/1424-8220/25/2/365SLAMlifelong mappinglocalizationoffline processingparallel mapping |
| spellingShingle | Francisco J. Romero-Ramirez Miguel Cazorla Manuel J. Marín-Jiménez Rafael Medina-Carnicer Rafael Muñoz-Salinas Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing Sensors SLAM lifelong mapping localization offline processing parallel mapping |
| title | Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing |
| title_full | Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing |
| title_fullStr | Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing |
| title_full_unstemmed | Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing |
| title_short | Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing |
| title_sort | parallelized slam enhancing mapping and localization through concurrent processing |
| topic | SLAM lifelong mapping localization offline processing parallel mapping |
| url | https://www.mdpi.com/1424-8220/25/2/365 |
| work_keys_str_mv | AT franciscojromeroramirez parallelizedslamenhancingmappingandlocalizationthroughconcurrentprocessing AT miguelcazorla parallelizedslamenhancingmappingandlocalizationthroughconcurrentprocessing AT manueljmarinjimenez parallelizedslamenhancingmappingandlocalizationthroughconcurrentprocessing AT rafaelmedinacarnicer parallelizedslamenhancingmappingandlocalizationthroughconcurrentprocessing AT rafaelmunozsalinas parallelizedslamenhancingmappingandlocalizationthroughconcurrentprocessing |