Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing

Francisco J. Romero-Ramírez

doi:10.3390/s25020365

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Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing

2025-01-09

Francisco J. Romero-Ramírez

Miguel Cazorla

Manuel J. Marín‐Jiménez

R. Medina-Carnicer

Rafael Muñoz‐Salinas *

DOI：10.3390/s25020365

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Abstract

En 中文

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.

Keywords:

SLAM

lifelong mapping

localization

offline processing

parallel mapping

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