Title :
UKF based vision aided navigation system with low grade IMU
Author :
Won, Dae Hee ; Sung, Sangkyung ; Lee, Young Jae
Author_Institution :
Dept. of Aerosp. Inf. Eng., Konkuk Univ., Seoul, South Korea
Abstract :
When integrating single vision sensor and low grade IMU for 6-DOP navigation, nonlinearity of observation model makes a problem to estimate position, velocity and attitude. Conventional Kalman Filter could not estimate states correctly because it uses linearized model. Due to these reasons, nonlinear estimation should be used to figure out the nonlinear characteristics. By applying Unscented Kalman Filter, this paper copes with the nonlinearity. The estimation performance is demonstrated by numerical simulation. The RMS error of estimated position is analyzed by comparing Extended Kalman Filter results.
Keywords :
Kalman filters; attitude control; computer vision; inertial navigation; mean square error methods; nonlinear estimation; nonlinear filters; position control; state estimation; velocity control; 6-DOP navigation; RMS error; UKF based vision aided navigation system; attitude estimation; extended Kalman filter; linearized model; low grade IMU; nonlinear characteristics; nonlinear estimation; nonlinearity; observation model; position estimation; single vision sensor; state estimation; unscented Kalman filter; velocity estimation; Adaptation model; Estimation; Kalman filters; Machine vision; Mathematical model; Navigation; Vehicles; IMU; Navigation; UKF; Vision;
Conference_Titel :
Control Automation and Systems (ICCAS), 2010 International Conference on
Conference_Location :
Gyeonggi-do
Print_ISBN :
978-1-4244-7453-0
Electronic_ISBN :
978-89-93215-02-1
