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Decentralized state estimation: An approach using pseudomeasurements and preintegration.

Charles Champagne Cossette1, Mohammed Ayman Shalaby1, David Saussié2

  • 1Department of Mechanical Engineering, McGill University, Montreal, QC, Canada.

The International Journal of Robotics Research
|October 8, 2024
PubMed
Summary

This study introduces pseudomeasurements for decentralized state estimation in robotic teams, enabling efficient communication and robust observability testing. A deep autoencoder further reduces communication needs for collaborative robotics.

Keywords:
Lie groupsRelative position estimationcollaborative localizationmulti-robot systemspreintegrationstate estimation

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Area of Science:

  • Robotics
  • Control Theory
  • Distributed Systems

Background:

  • Decentralized state estimation is crucial for multi-robot systems.
  • Existing methods face challenges in communication efficiency and observability analysis.
  • Robots often need to estimate similar physical quantities, like relative positions.

Purpose of the Study:

  • To develop a novel framework for decentralized, collaborative state estimation in robotic teams.
  • To introduce pseudomeasurements for modeling inter-robot state relationships.
  • To enhance communication efficiency and observability testing in multi-robot systems.

Main Methods:

  • Introduced pseudomeasurements to model relationships between robots' state estimates.
  • Developed a general-purpose observability test considering sensor measurements and communication structure.
  • Proposed input preintegration for communication-efficient odometry sharing.
  • Utilized a deep autoencoder to reconstruct covariance information, reducing communication overhead.
  • Applied the framework to both vector-space and Lie-group state definitions.

Main Results:

  • Demonstrated the tractability of decentralized state estimation using pseudomeasurements.
  • Showcased a unified observability test for decentralized robotic systems.
  • Validated the communication efficiency of input preintegration and deep autoencoder-based covariance reconstruction.
  • Successfully evaluated the framework on simulated problems and a quadcopter experiment.

Conclusions:

  • The proposed pseudomeasurement framework offers a tractable and effective solution for decentralized collaborative state estimation.
  • The developed methods significantly improve communication efficiency and observability analysis in robotic teams.
  • The framework is versatile, applicable to various state definitions and validated in practical scenarios.