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

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.

本文提出了一种新颖的方法，该方法使用一个位置传感器和一个惯性测量单元来估计位置和方向。所提出的方法使用粒子滤波器估计方向，并使用卡尔曼滤波器（KF）估计位置和速度。此外，使用一个专家系统来校正角速度测量误差。实验结果表明，与扩展卡尔曼滤波器（EKF）方法获得的方向误差相比，使用所提方法的方向误差显著降低。使用所提方法改进的方向估计可带来更好的位置估计精度。本文研究了所提滤波器的粒子数量和位置传感器噪声对方向精度的影响。此外，实验结果表明，即使初始方向完全未知，所提方法的方向也能收敛到正确方向。