Integrated Velocity Measurement Algorithm Based on Optical Flow and Scale-Invariant Feature Transform

• Main Authors: Xiaochen Liu, Xiaoting Guo, Donghua Zhao, Huiliang Cao, Jun Tang, Chenguang Wang, Chong Shen, Jun Liu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Cubature Kalman filter; optical flow; residual error correction; scale-invariant feature transform
• Online Access: https://ieeexplore.ieee.org/document/8879496/

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.