Abstract
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.
Highlights
IntroductionFor an environment with accurate and persistent absolute measurements, it is easy to achieve highly accurate estimates of robots’ poses
0.28 where RMSEσr and RMSEσθ denote, respectively, the root mean square errors (RMSEs) of standard deviations of range and bearing measurements, and lki∗(s) and jki∗(s) denote, respectively, the detected landmark and robot sets by robot i at time k of the s-th Monte Carlo simulation, and |lki∗(s)| denotes the number of landmarks within the set lki∗(s), and |jki∗(s)| denotes the number of mobile robots within the set jki∗(s), and σril,(ask)|k and σθil,(ask)|k denote the estimates of standard deviations of absolute range and bearing measurements at time k of the s-th Monte Carlo simulation, respectively, and σrij,r(ks)|k and σθij,r(sk)|k denote the estimates of standard deviations of relative range and bearing measurements at time k of the s-th Monte Carlo simulation, respectively
In this paper, a novel adaptive recursive DCL (RDCL)-extended Kalman filter (EKF) method was proposed for multi-robot systems with time-varying measurement accuracy, where a novel decentralized estimation strategy was proposed for estimating the unknown absolute and relative measurement noise covariance matrices in a decentralized manner
Summary
For an environment with accurate and persistent absolute measurements, it is easy to achieve highly accurate estimates of robots’ poses. If the global position system (GPS) signal is accessible, the resultant integration of odometry and GPS can provide accurate estimates of robots’ poses. For an environment with intermittent absolute measurements or without absolute measurements, it is a challenge to achieve accurate estimates of poses [7]. Cooperative localization (CL) is a promising technique to improve the localization accuracy of multi-robot systems for the above scenarios [8], [9]. With CL, the accurate absolute localization information obtained by some team members can be spread to other members through information exchanges and relative measurements, based on which the multi-robot systems can achieve improved localization accuracy [8], [10]
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