Autonomous Velocity Measurement And Positioning Technology Based On Improved Optical Flow Algorithm

In recent years,unmanned aerial vehicles（UAV）have attracted worldwide attention for its advantages of small size and low cost.As a key technology of UAV,navigation technology is an important guarantee for UAV to realize autonomous flight,unmanned reconnaissance and precision strikes.This paper is geared towards satellite denial environments,the optical flow method is used to measure the velocity,and the position information of unmanned aerial vehicle is obtained by velocity integration.However,the optical flow method is easily influenced by variation of illumination,resulting in velocity measurement errors.And the position information is obtained by integration,there is inevitably a cumulative error,resulting in inaccurate velocity and position information measured.In order to further improve the accuracy of velocity measurement and positioning of the UAV,this paper mainly studies the velocity measurement method based on the optical flow/ORB algorithm,and the position information measurement method based on the navigation model of animal brain’s place cells.The feasibility of the proposed method is verified by unmanned aerial vehicle.The main work of this paper is as follows:（1）This paper introduces the traditional optical flow and ORB algorithm,and expounds the applicability,advantages and disadvantages of several optical flow algorithm;And considering that the accuracy of ORB algorithm is relatively poor,and to improve the velocity accuracy of the ORB algorithm,the RANSAC algorithm and the K-means clustering algorithm are used to optimize ORB algorithm.（2）Aiming at the problem that optical flow algorithm is sensitive to the variation of illumination,a visual velocity measurement method based on square root cubature Kalman filter is proposed in this paper.The information of optical flow and ORB algorithm are fused by square root cubature Kalman filter to reduce the interference of the variation of illumination to the velocity measurement.The testing results of airborne show that the proposed optical flow/ORB combined velocity measurement method can effectively improve the accuracy measurement accuracy of the carrier.（3）In order to solve the problem of error accumulation in the path integration process of optical flow/ORB combined velocity measurement system,the navigation mechanism of place cells in the hippocampus of the animal brain is used to corrected the accumulated position error through externally perceived visual information.Finally,the testing results of airborne is carried out to verify the error correction algorithm proposed in this paper.The results show that the proposed method can provide accurate position information for the carrier.