Adaptive Noise Identification in Vision-assisted Motion Estimation for Unmanned Aerial Vehicles

作     者：Fan Zhou Wei Zheng Zeng-Fu Wang 

作者机构：Department of AutomationUniversity of Science and Technology of China Institute of Intelligent MachinesChinese Academy of Sciences 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2015年第12卷第4期

页      面：413-420页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Science and Technology Major Projects of the Ministry of Science and Technology of China:ITER(No.2012GB102007) 

主　　题：Adaptive noise variance identification vision location motion estimation Kalman filter unmanned aerial vehicle. 

摘      要：Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the *** the noise of the vision location result is affected by external environment,the variance of the noise is ***,in previous researches,the variance is usually set as a fixed empirical value,which will lower the accuracy of the motion *** main contribution of this paper is that we proposed a novel adaptive noise variance identification(ANVI) method,which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the *** adaptively identified variance is used in the Kalman filter for more accurate motion *** performance of the proposed method is assessed by simulations and field experiments on a quadrotor *** results illustrate the effectiveness of the method.