Autonomous Flight Control Design Based on Multi-Sensor Fusion for a Low-Cost Quadrotor in GPS-Denied Environments

Abstract

This paper presents a fully autonomous and low-cost quadrotor helicopter system in GPS-denied environments. An extended Kalman filter (EKF) framework is employed to fuse the raw data from inertial measurement unit (IMU), optical flow, ultrasonic rangefinder to achieve real-time state estimation of the quadrotor. An improved cascaded proportional-integral-derivative (PID) control scheme is proposed to achieve high-precision flight control for the quadrotor. A lightweight, powerful app of DroneKit-Python is integrated to directly guide the flight mission of the quadrotor on an onboard computer. The onboard computer which communicates with the flight controller using the MAVLink protocol augments the performance of the autopilot by a low-latency link. Finally, the autonomous quadrotor system is justified by real-world experiments, and the experimental results show that the quadrotor can realize high-precision autonomous localization in an indoor environment.