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Parallelized SLAM: Enhancing Mapping and Localization Through Concurrent Processing.

Francisco J Romero-Ramirez1, Miguel Cazorla2, Manuel J Marín-Jiménez3,4

  • 1Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Campus de Fuenlabrada, Universidad Rey Juan Carlos, 28942 Fuenlabrada, Spain.

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Summary
This summary is machine-generated.

This study introduces parallel mapping (pSLAM) to accelerate Simultaneous Localization and Mapping (SLAM) map creation. pSLAM significantly speeds up map generation for real-time navigation on less powerful devices.

Keywords:
SLAMlifelong mappinglocalizationoffline processingparallel mapping

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) systems require substantial computational resources, limiting real-time applications on low-end hardware.
  • Current offline map creation for SLAM is sequential and time-consuming, even on powerful computers.

Purpose of the Study:

  • To develop a parallel mapping method (pSLAM) to significantly reduce the time required for offline SLAM map generation.
  • To enable faster deployment of SLAM for real-time navigation on resource-constrained devices.

Main Methods:

  • The proposed pSLAM method partitions video sequences into multiple subsequences for independent processing.
  • Individual submaps generated from subsequences are merged to create a unified global map.
  • Experimental validation across diverse scenarios and comparison with state-of-the-art SLAM methods (UcoSLAM, OpenVSLAM, ORB-SLAM3).

Main Results:

  • pSLAM achieved processing speeds up to 6 times faster than sequential approaches.
  • The method maintained robustness comparable to traditional sequential SLAM.
  • pSLAM outperformed UcoSLAM, OpenVSLAM, and ORB-SLAM3 in all evaluated scenarios.

Conclusions:

  • Parallel processing significantly accelerates offline map creation for SLAM.
  • pSLAM offers a viable solution for efficient real-time navigation using pre-generated maps on low-end systems.
  • The proposed method demonstrates superior performance and speed compared to existing state-of-the-art SLAM techniques.