Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

Decentralized cooperative localization (DCL) is a promising method to determine accurate multirobot poses (i.e., positions and orientations) for robot teams operating in an environment without absolute navigation information. Existing DCL methods often use fixed measurement noise covariance matrices...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-25
Main Authors: Huang, Yulong, Xue, Chao, Zhu, Fengchi, Wang, Wenwu, Zhang, Yonggang, Chambers, Jonathon A.
Format: Article
Language:English
Subjects:
Accuracy
Adaptive filter
Adaptive systems
Bayesian analysis
Covariance matrices
Covariance matrix
decentralized cooperative localization
Estimation
extended Kalman filter
Localization
Location awareness
Multi-robot systems
Multiple robots
multirobot systems
Noise
Noise measurement
Performance degradation
Robots
Time measurement
variational Bayesian
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Summary:Decentralized cooperative localization (DCL) is a promising method to determine accurate multirobot poses (i.e., positions and orientations) for robot teams operating in an environment without absolute navigation information. Existing DCL methods often use fixed measurement noise covariance matrices for multirobot pose estimation; however, their performance degrades when the measurement noise covariance matrices are time-varying. To address this problem, in this article, a novel adaptive recursive DCL method is proposed for multi-robot systems with time-varying measurement accuracy. Each robot estimates its pose and measurement noise covariance matrices simultaneously in a decentralized manner based on the constructed hierarchical Gaussian models using the variational Bayesian approach. Simulation and experimental results show that the proposed method has improved cooperative localization accuracy and estimation consistency but slightly heavier computational load than the existing recursive DCL method.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3054005