Unmanned Aerial Vehicle Position Estimation Augmentation Using Optical Flow Sensor

Abstract

Flight control of unmanned aerial vehicle (UAV) requires reliable measurements of UAV’s position and attitude. Optical flow sensor is able to detect UAV’s motion with respect to the ground, and thus it can be incorporated in the integrated navigation system to enhance the positioning accuracy. However, the commonly used measurement model of optical flow sensor is actually fit for continuous-time condition only, since it defines the optical flow as the instantaneous velocity of a pixel on the image plane. For commercial optical flow sensors that work under discrete-time condition, a novel measurement model is proposed in this paper, which gives a vectorized symmetrical description of the optical flow measurement between every two successive frames in the image sequence. Moreover, a cubature transform-based data fusion scheme is also presented in this paper, which can directly augment the UAV’s position estimation with optical flow data, rather than extracting UAV’s velocity information from optical flow for dead-reckoning, and hence it can be easily added to the UAV’s navigation system without changing the existing algorithm flow. Flight tests are conducted using a quadcopter UAV that equipped with PIXHAWK autopilot and PX4Flow optical flow sensor, and the test results prove that the proposed optical flow model and data fusion scheme can effectively improve the accuracy of UAV position estimation in various outdoor environments.