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Multi-Robot Cooperative Simultaneous Localization and Mapping Algorithm Based on Sub-Graph Partitioning.

Wan Xu1, Yanliang Chen1, Shijie Liu1

  • 1School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.

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|May 14, 2025
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Summary
This summary is machine-generated.

This study enhances multi-robot collaborative Simultaneous Localization and Mapping (SLAM) by improving loop closure detection and pose optimization. The new methods boost efficiency and accuracy for coordinated robot navigation.

Keywords:
collaborative SLAMdistributed optimizationloop closure detectionmulti-robot systemsmulti-sensor fusion

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Multi-robot collaborative SLAM faces challenges in computational efficiency and accuracy.
  • Front-end loop detection suffers from redundant computations and inefficient candidate selection.
  • Back-end pose optimization exhibits high complexity and long iteration times.

Purpose of the Study:

  • To improve computational efficiency and localization accuracy in multi-robot collaborative SLAM.
  • To mitigate redundant computations and enhance processing speed in loop detection.
  • To reduce complexity and iteration duration in global pose optimization.

Main Methods:

  • Implemented a global matching and candidate loop selection strategy using LiDAR and visual features for cross-robot loop detection.
  • Developed an improved distributed robust pose graph optimization algorithm.
  • Introduced a robust cost function for erroneous loop closure filtering and subgraph optimization for faster convergence.

Main Results:

  • Front-end loop detection achieved an F1-score improvement of 8.5-51.5%.
  • Back-end optimization demonstrated superior convergence speed and accuracy compared to traditional methods.
  • Overall localization accuracy improved by approximately 32.8% over open-source algorithms.

Conclusions:

  • The proposed enhancements significantly improve the efficiency and accuracy of multi-robot collaborative SLAM.
  • The integrated front-end and back-end improvements effectively address computational bottlenecks and optimize pose estimation.
  • This work provides a more robust and efficient solution for coordinated multi-robot navigation and mapping.