Shengduo Chen

Shengduo Chen

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Multi-robot Feature-based SLAM using Submap Joining

Published in Astralasian Conference on Robotics and Automation, 2021

This paper considers the feature-based SLAM using multiple robots. To reduce the computational complexity and data storage, a distributed multi-robot feature-based SLAM algorithm under submap joining scheme is proposed. Each robot first independently builds a submap using the information collected by its sensors. Once the robots can observe each other, the submaps can then be fused together to obtain a global map. We implemented and tested the proposed algorithm in both simulation and real world environments. Both simulation and experimental results have validated the robustness and accuracy of the proposed algorithm.

Recommended citation: 1. S. Chen, R. Han, L. Zhao, S. Huang and Q. Hao, "Multi-robot Feature-based SLAM using Submap Joining," In Australasian Conference on Robotics and Automation, ACRA Online, 2021, 2021-December, pp. 1-8
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Paper Title Number 4

Published in GitHub Journal of Bugs, 2024

This paper is about fixing template issue #693.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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