On the consistency of multi-robot cooperative localization
On the consistency of multi-robot cooperative localization
复制标题
多机器人协作定位的一致性研究
DOI：
10.15607/rss.2009.v.009
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
S. Roumeliotis
中科院分区：
文献类型：
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作者：
Guoquan P. Huang;N. Trawny;Anastasios I. Mourikis;S. Roumeliotis
本文从可观测性的角度研究了基于扩展卡尔曼滤波（EKF）的协同定位（CL）的一致性。据我们所知，这是第一次解析地表明，在基于ekf的标准CL中所采用的误差状态系统模型总是具有比实际非线性CL系统更高维的可观察子空间。这将导致在没有可用信息的状态空间方向上不合理地减少EKF协方差估计，从而导致不一致。为了解决这个问题，我们采用了一种基于可观察性的方法来设计一致性估计器，并提出了一种新的可观察性约束(OC)-EKF。与标准EKF-CL相比，OC-EKF的线性化点的选择是为了保证可观测子空间的维数与原（非线性）系统的维数保持一致。所提出的OC-EKF已经在模拟和实验中进行了测试，并且在准确性和一致性方面都明显优于标准EKF。
In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. To the best of our knowledge, this is the first work that analytically shows that the error-state system model employed in the standard EKF-based CL always has an observable subspace of higher dimension than that of the actual nonlinear CL system. This results in unjustified reduction of the EKF covariance estimates in directions of the state space where no information is available, and thus leads to inconsistency. To address this problem, we adopt an observabilitybased methodology for designing consistent estimators and propose a novel Observability-Constrained (OC)-EKF. In contrast to the standard EKF-CL, the linearization points of the OC-EKF are selected so as to ensure that the dimension of the observable subspace remains the same as that of the original (nonlinear) system. The proposed OC-EKF has been tested in simulation and experimentally, and has been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.