Multi-Sensory Motion Estimation and Control of an Autonomous Quadrotor

Abstract

In this paper, a fully autonomous quadrotor in a heterogeneous air–ground multi-robot system is established only using minimal on-board sensors: a monocular camera and inertial measurement units (IMUs). Efficient pose and motion estimation is proposed and optimized. A continuous-discrete extended Kalman filter is applied, in which the high-frequency IMU data drive the prediction, while the estimates are corrected by the accurate and steady vision data. A high-frequency fusion at 100 Hz is achieved. Moreover, time delay analysis and data synchronizations are conducted to further improve the pose/motion estimation of the quadrotor. The complete on-board implementation of sensor data processing and control algorithms reduces the influence of data transfer time delay, enables autonomous task accomplishment and extends the work space. Higher pose estimation accuracy and smaller control errors compared to the standard works are achieved in real-time hovering and tracking experiments.