KFPA Monocular Ranging Algorithm Design and Application in Mobile edge Computing

Abstract

Distance perception is the basis and necessary prerequisite of environment perception, attitude perception and obstacle avoidance for both intelligent vehicle and unmanned vehicle. Passive ranging is a critical part of machine vision measurement. Most passive ranging methods based on machine vision apply binocular technology that needs strict hardware conditions and lacks universality. Therefore, the monocular vision ranging method is one of the mainstream distance sensing methods at present. In order to improve the accuracy of monocular vision ranging, a monocular vision ranging method based on pixel area and aspect ratio is proposed. Subsequently, this method improves the stability of real-time target detection by introducing Kalman filter processing. Experimental results display that that ranging used by this method has higher accuracy. The mean relative error of the depth measurement is 5% when it is 3-10 m. After introducing Kalman filter, the stability of real-time ranging processing is improved by 25.21%. In this paper, the ROS smart car with real-time target tracking is also realized by the method based on the combination of SIFT-KCF target detection and tracking and monocular ranging.