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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6835456, 11 pages
https://doi.org/10.1155/2017/6835456
Research Article

A Vision/Inertia Integrated Positioning Method Using Position and Orientation Matching

School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China

Correspondence should be addressed to Xiaoyue Zhang

Received 31 December 2016; Revised 26 February 2017; Accepted 9 April 2017; Published 14 May 2017

Academic Editor: Jason Gu

Copyright © 2017 Xiaoyue Zhang and Liang Huo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A vision/inertia integrated positioning method using position and orientation matching which can be adopted on intelligent vehicle such as automated guided vehicle (AGV) and mobile robot is proposed in this work. The method is introduced firstly. Landmarks are placed into the navigation field and camera and inertial measurement unit (IMU) are installed on the vehicle. Vision processor calculates the azimuth and position information from the pictures which include artificial landmarks with the known direction and position. Inertial navigation system (INS) calculates the azimuth and position of vehicle in real time and the calculated pixel position of landmark can be computed from the INS output position. Then the needed mathematical models are established and integrated navigation is implemented by Kalman filter with the observation of azimuth and the calculated pixel position of landmark. Navigation errors and IMU errors are estimated and compensated in real time so that high precision navigation results can be got. Finally, simulation and test are performed, respectively. Both simulation and test results prove that this vision/inertia integrated positioning method using position and orientation matching has feasibility and it can achieve centimeter-level autonomic continuous navigation.