Title :
Vision Aided Inertial Navigation with Measurement Delay for Fixed-Wing Unmanned Aerial Vehicle Landing
Author :
Joo, Sungmoon ; Ippolito, Corey ; Al-Ali, Khalid ; Yeh, Yoo-Hsiu
Author_Institution :
Dept. of Aeronaut. & Astronaut., Stanford Univ., Stanford, CA
Abstract :
Usually, standard inertial navigation unit (INU) with global positioning system (GPS) provides relatively poor accuracy in altitude estimation, while autonomous landing of unmanned aerial vehicles (UAVs) requires accurate position estimation. In this paper, a UAV navigation system with aid from an external camera for landing is investigated. This paper presents: (i) a sensor fusion algorithm for passive monocular vision and INU based on the extended Kalman filter (EKF) considering measurement delay to improve the accuracy of position estimates, and (ii) a robust object-detection vision algorithm using optical flow. Pilot controlled landing experiments on a NASA UAV platform and the filter simulations validate the feasibility and performance of the proposed approach.
Keywords :
Global Positioning System; Kalman filters; aerospace computing; aircraft control; computer vision; inertial navigation; nonlinear filters; remotely operated vehicles; sensor fusion; GPS; Global Positioning System; altitude estimation; extended Kalman filter; fixed-wing unmanned aerial vehicle landing; measurement delay; optical flow; passive monocular vision; robust object-detection vision algorithm; sensor fusion algorithm; vision aided inertial navigation; Cameras; Delay estimation; Fluid flow measurement; Global Positioning System; Inertial navigation; Optical filters; Position measurement; Robustness; Sensor fusion; Unmanned aerial vehicles;
Conference_Titel :
Aerospace Conference, 2008 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
978-1-4244-1487-1
Electronic_ISBN :
1095-323X
DOI :
10.1109/AERO.2008.4526557
