Wei Jiaqi, Liu Jun, Tang Jun, Yu Hua, Shen Chong, Lu Zhumao, Zhao Donghua, Wang Chenguang, Bai Yang
Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education, School of Instrument and Electronics, North University of China, Taiyuan 030051, People's Republic of China.
State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, People's Republic of China.
Rev Sci Instrum. 2022 Jan 1;93(1):015004. doi: 10.1063/5.0062076.
The velocity measurement algorithm based on vision is widely used in unmanned aerial vehicle navigation. Under uneven illumination intensity distribution, the traditional Lucas-Kanade (LK) optical flow (OF) algorithm has problems arising from low computational accuracy and poor adaptability. To solve these problems, we propose a monocular vision integrated velocity measurement system based on the square-root cubature Kalman filter (SRCKF). The LK OF and the optimized oriented FAST and rotated BRIEF (ORB) algorithms are used to process the visual information obtained using a camera. The SRCKF algorithm is tasked with fusing the LK OF and optimized ORB information, thereby improving the accuracy of velocity and alleviating the sensitivity of the LK OF to variations in illumination conditions. Finally, an outdoor unmanned aerial vehicle flight test was undertaken. The experimental results show that the proposed method provides an accurate measurement of the velocity in variable illumination environments.
