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Most of quadcopter operate in the outdoor environment where is often complex and unpredicted. In order to control the quadcopter in unknown outdoor environment, it should be designed an excellent filter to estimate complete state vector which illustrates the movement of rigid body. In this paper, two filters such as Complementary and Kalman are investigated to compare the performance. Quadcopter collect only measurements from a low-cost inertial measurement unit, IMU-MPU6050. The raw data is put into filter to estimate the state vector in system. In the simulations, these filters are studied to implement in the model of quadrotor. Later, the hardware platform of quadcopter is built to denote experimental results in order to validate the effective design of Complementary filter and Kalman filter in quadcopter.
Ngo et al. (Sat,) studied this question.