Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

A Noordin,M A M Basri,Z Mohamed

doi:10.12928/telkomnika.v16i2.9020

Abstract

This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

Highlights

Nowadays, advanced in technology innovates to combine mechanical and electrical components into micro-scale object calls as Microelectromechanical systems (MEMS) [1], [2]
With MEMS technology component being inexpensive, small size, low power consumption, products are redesigned to include such as an inertial measurement unit (IMU) sensor onto telecommunication, automotive industries and medical application [3,4,5,6]
MEMS technology are the main of attitude and heading reference system (AHRS) to determine rotation, motion, location and direction of mobile robots for automated navigation, autonomous underwater vehicle (AUV) for global localization systems and unmanned aerial vehicle (UAV) in aviation [1], [6,7,8,9]

Summary

Introduction

Nowadays, advanced in technology innovates to combine mechanical and electrical components into micro-scale object calls as Microelectromechanical systems (MEMS) [1], [2]. With MEMS technology component being inexpensive, small size, low power consumption, products are redesigned to include such as an inertial measurement unit (IMU) sensor onto telecommunication, automotive industries and medical application [3,4,5,6]. Most researcher applied sensor fusion algorithms techniques to overcome the measurement errors and obtaining accurate reading [1], [6], [7], [10]. In present-day, nonlinear filter techniques for instance extended kalman filter (EKF) [1], [11,12,13], unscented kalman filter (UKF) [14,15,16,17] and complementary filter (CF) [18,19,20,21] have been developed, reformed and integrated to obtain the optimal fusion of sensors or in other term called multi fusion integration (MFI) [22]-[23]

Methods

Results

Conclusion