Attitude Estimation by Kalman Filter Based on the Integration of IMU and Multiple GPSs and Its Application to Connected Drones

We designed a connected drone that can be connected to surrounding drones by electromagnets. However, since the magnetic field was disturbed by the magnetic force of the electromagnets, the magnetic sensors could not be used. Thus, since magnetic sensors play an essential role in the attitude estimation of drones, the problem was that the drone could not perform high-precision autonomous flying control. As a result, to substitute the use of magnetic sensors, this study proposes a method of integrating the IMU and multiple GPSs using the Kalman filter. The proposed method can perform highly accurate attitude estimation by adding the velocity vector to the observation vector. First, a comparison experiment shows that the Kalman filter is the most suitable method for the attitude estimation of connected drones among other methods, including the Q-Method, Madgwick filter, and Kalman filter. Next, we show that the addition of the velocity vector improved the roll and pitch angle estimation accuracy by about 10% and the yaw angle estimation accuracy by about 5% in comparison with the conventional method.

1. INTRODUCTION

Due to the enthusiastic boom of domestic and overseas drones after 2015, more people have noticed the industrial applications of drones, and their utilization by a single machine seems to have almost reached the stage of maturity. On the other hand, since drones have limits in their payloads and flight ranges, they are limited to small loads in tasks that include the transportation of loads. To overcome such limitations, it was assumed to increase the payload by enlarging the motor and propeller. However, this method was not preferable because of the risk of falling and the extra cost of development and operation.