Real-Time Nonlinear Complementary Filter on SO(3) for Attitude Estimation of Small-Scale Aerial Robot

Abstract

This paper presents the real-time implementation of a powerful nonlinear complementary filter on special orthogonal group of rotation matrices, called as NCF SO(3) for attitude estimation. It fuses the raw data from accelerometers, magnetometer, and gyroscopes sensors to get reliable real-time attitude estimation. Gyroscopes is used as the main sensor for attitude estimation and another two sensors are used to correct drift error of gyroscopes. In this paper, the performance of NCF SO(3) is explored on performance in highly dynamic manoeuvres in real-time. Real-time experiments were conducted to compare its performance with conventional Extended Kalman Filter (EKF) to exploit the positive features of NCF SO(3) for small-scale aerial robot with limited on-board processor memory cases. The experimental results show the proposed real-time filter has excellent estimated attitude data and can reduce the computational cost, compared to EKF. Thus, it is suitable for small-scale aerial robot which has memory limitation of on-board processor.