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Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey.

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  • 1School of Information, Renmin University of China, Beijing 100872, China.

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Summary
This summary is machine-generated.

This survey explores distributed relative localization for robot networks, focusing on algorithms that use local measurements for robust, low-communication navigation. It categorizes methods and discusses supporting research for improved performance.

Keywords:
distributed SLAMdistributed localizationmulti-robot systemsrelative localization

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Area of Science:

  • Robotics
  • Networked Systems
  • Distributed Computing

Background:

  • Relative localization is crucial for multi-robot tasks.
  • Traditional methods face communication latency and fragility.
  • Distributed algorithms offer lower communication burden and enhanced robustness.

Purpose of the Study:

  • To provide a comprehensive survey of distributed relative localization methodologies for robot networks.
  • To classify and analyze existing distributed localization algorithms.
  • To review supporting research areas and simulation platforms.

Main Methods:

  • Classification of algorithms based on measurement types: distance-based, bearing-based, and multiple-measurement-fusion-based.
  • Detailed analysis of design methodologies, advantages, drawbacks, and applications.
  • Survey of research on local network organization, communication efficiency, and robustness.

Main Results:

  • Categorization of distributed relative localization algorithms.
  • Summary of design considerations, performance trade-offs, and use cases.
  • Overview of research supporting distributed localization and relevant simulation tools.

Conclusions:

  • Distributed relative localization is essential for efficient and robust multi-robot systems.
  • Algorithm design, communication protocols, and network organization are key challenges.
  • Further research and simulation are needed to advance the field.