A Kalman/Particle Filter-Based Position and Orientation Estimation Method Using a Position Sensor/Inertial Measurement Unit Hybrid System

Abstract

This paper presents a novel methodology that estimates position and orientation using one position sensor and one inertial measurement unit. The proposed method estimates orientation using a particle filter and estimates position and velocity using a Kalman filter (KF). In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results show that the orientation errors using the proposed method are significantly reduced compared to the orientation errors obtained from an extended Kalman filter (EKF) approach. The improved orientation estimation using the proposed method leads to better position estimation accuracy. This paper studies the effects of the number of particles of the proposed filter and position sensor noise on the orientation accuracy. Furthermore, the experimental results show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown.