Localization of Indoor Mobile Robot Using Minimum Variance Unbiased FIR Filter
The demand of indoor localization has recently grown quickly in industries. In general, a localization system is required to be reliable, fast, and have high accuracy. In this paper, the ultrawideband (UWB) technique is combined with the inertial navigation sensor (INS) to form a coupled UWB/INS localization framework, which inherits the advantages from both components. A minimum variance unbiased finite impulse response (MVU FIR) method is then applied to obtain accurate position and velocity estimations from noisy measurements. Two experiments and several simulations are conducted. Compared with the traditional Kalman filter (KF) and particle filter, the MVU FIR filter exhibits better immunity to the errors about a priori knowledge of noise variances. It can handle the kidnapped problem, and recover from some extreme failures satisfactorily. Moreover, the MVU FIR filtering algorithm is fast and easily implementable. Its online computational time is even lower than that of the KF, which is favorable in localization applications.Note to Practitioners For indoor robot localization, an effective filtering algorithm can improve the accuracy significantly. Existing and commonly used filtering approaches suffer from weak robustness to the imprecise a priori knowledge of noise variances used, unsatisfied performance when dealing with sudden behaviors of robot, and large online computational load. This paper suggests a new practical method to estimate the current state using finite past measurements with a finite impulse response filter gain computed off line. By testing it within the coupled ultrawideband/inertial navigation sensor localization framework under different operating conditions, we demonstrate that the proposed method can effectively overcome the above problems, providing fast, accurate, and reliable estimation in a practical environment.
IEEE Transactions on Automation Science and Engineering