Title :
Vision aided INS for UAV auto landing navigation using SR-UKF based on two-view homography
Author :
Cai Ming ; Sun Xiu-xia ; Xu Song ; Liu Xi
Author_Institution :
Sch. of Aeronaut. & Astronaut. Eng., Air Force Eng. Univ., Xian, China
Abstract :
An SR-UKF model for integrating INS and Vision navigation system was built to estimate the INS system error, in which regarding the INS system error equation as process model, and homography between two-view as measure model. Aiming at the invalidity of traditional homography between two views when coplanar points exactly in the word coordinate system plane, a novel expression of homography was proposed under the certain circumstances like UAV auto landing based on vision technology. To improve the efficiency and stability of filter, SR-UKF was used for states estimating, and the navigation data was compensated by the estimated result. Simulations show that the method proposed was effective to improve the accuracy of navigation system.
Keywords :
Kalman filters; autonomous aerial vehicles; computer vision; inertial navigation; nonlinear filters; INS system error equation; SR-UKF model; UAV auto landing navigation; coplanar points; navigation data; process model; states estimating; two-view homography; vision navigation system; vision technology; word coordinate system plane; Cameras; Conferences; Educational institutions; Geometry; Inertial navigation; Mathematical model;
Conference_Titel :
Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4799-4700-3
DOI :
10.1109/CGNCC.2014.7007276