Integrated vision/inertial navigation method of UAVs in indoor environment

被引：0
|
作者：
Wang T. [1 ]
Cai Z. [1 ,2 ]
Wang Y. [1 ,2 ]
机构：
[1] School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing
[2] Aircraft Control Integration National Defense Key Laboratory, Beijing University of Aeronautics and Astronautics, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2018年 / 44卷 / 01期
关键词：
Multi-sensor fusion; Optical flow; ORB features; UAV; Vision navigation;
D O I：
10.13700/j.bh.1001-5965.2016.0965
中图分类号：
学科分类号：
摘要：
A new integrated navigation method based on inertial sensor, optical flow and visual odometry is proposed for self-navigation indoor in GPS-denied environment. An ORB optical flow based method is also proposed for estimating real-time three-axis velocity of the UAV. The algorithm improves the traditional pyramid Lucas-Kanade method using sparse optical flow based on feature points. The tracking of feature points is made more accurate by applying forward-backward tracking and random sampling consensus strategies. For position estimation, a visual odometry method with integrated vision/inertial navigation is adopted, which uses the artificial icon method, visual optical flow information and inertial navigation data. Finally, the velocity and position estimations from the proposed method are validated via actual flight test and via comparison with velocity measurement information from a PX4Flow module and a Guidance module and with locating information from movement capture system. © 2018, Editorial Board of JBUAA. All right reserved.
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页码：176 / 186
页数：10
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