The pyramid Lucas-Kanade (LK) optical flow algorithm has been widely used in velocity measurement applications. However, these applications are limited by some shortcomings of the algorithm, such as its slow calculation speed and susceptibility to illumination changes. To solve these problems, a data fusion scheme based on the scale-invariant feature transform (SIFT) and optical flow is proposed to alleviate the dependence of the optical flow on the illumination conditions. In addition, an improved cubature Kalman filter (CKF) based on multi-rate residual correction (CKF-MRC) is proposed to solve the problem of inconsistency between the sampling frequencies of the SIFT and the optical flow, and takes full advantage of the high sampling frequency of SIFT. The experimental results demonstrate that the proposed CKF-MRC method can effectively improve the accuracy of velocity measurement under variable illumination conditions with a high sampling frequency.

Introduction

In recent years, rapid progress has been made in the development of unmanned aerial vehicle (UAV) technology. UAVs have been widely used in fields such as reconnaissance, military strikes, aerial photography, mapping and emergency rescue [1]–[۵]. In addition, the optical flow navigation method inspired by insect motion is playing an increasingly important role in navigation in GPS-signal-denied environments. Optical flow (OF) can be regarded as the 2D projection movement of the 3D motion of observed objects [6]–[۱۰]. The bioinspired OF navigation scheme has been developed accordingly, with superior properties that include small device volume, low power requirements, high autonomy and low cost, which are especially important in UAV navigation applications [11]–[۱۳]. When OF is used as the sole navigation scheme, it can easily be disturbed by the surrounding environment, which leads to reduced navigation accuracy. Many researchers have made corresponding improvements to make the OF algorithm conform to a variety of environments. For example, [14] proposed an information fusion method based on a microelectromechanical systems-based inertial measurement unit (MEMS-IMU) and OF, which was used to correct the MEMSIMU’s attitude when it diverged; simulation results showed that modification of the vehicle attitude in combination with OF provided good performance, with the advantages of smaller errors, slow divergence and improved robustness.