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SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

Author

Listed:
  • Zhaoyi Pei

    (Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Songhao Piao

    (Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Mohammed El Habib Souidi

    (Department of Computer Science, University of Khenchela, Khenchela 40000, Algeria)

  • Muhammad Zuhair Qadir

    (Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Guo Li

    (Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

Abstract

The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping.

Suggested Citation

  • Zhaoyi Pei & Songhao Piao & Mohammed El Habib Souidi & Muhammad Zuhair Qadir & Guo Li, 2018. "SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation," Sustainability, MDPI, vol. 10(8), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2946-:d:164578
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    Cited by:

    1. Ching Sing Chai & Thomas K. F. Chiu & Xingwei Wang & Feng Jiang & Xiao-Fan Lin, 2022. "Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory," Sustainability, MDPI, vol. 15(1), pages 1-16, December.

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