吉林大学学报(信息科学版) ›› 2016, Vol. 34 ›› Issue (6): 774-780.

• 论文 • 上一篇    下一篇

基于单目视觉和惯性测量的飞行器自定位研究

王 巍 1 , 梁 桥 2   

  1. 1. 浙江经济职业技术学院 数字信息技术分院, 杭州 310018; 2. 网易杭州研究院 智能硬件部, 杭州 310052
  • 收稿日期:2016-09-13 出版日期:2016-11-25 发布日期:2017-01-16
  • 作者简介:王巍(1973— ), 女, 浙江金华人, 浙江经济职业技术学院讲师, 硕士, 主要从事计算机网络技术、 网络数据科学与技术研究, (Tel)86-15356166553(E-mail)wanggw_me@163. com。
  • 基金资助:
     国家自然科学基金资助项目(61070042); 浙江省自然科学基金资助项目(Y14F020075)

Research of Ego-Positioning for Micro Air Vehicles Based on Monocular Vision and Inertial Measurement

WANG Wei 1 , LIANG Qiao 2   

  1. 1. School of Digital Information Technology, Zhejiang Technical Institute of Economics, Hangzhou 310018, China;
    2. Intelligent Hardware Department, NetEase Hangzhou R&D Center, Hangzhou 310052, China
  • Received:2016-09-13 Online:2016-11-25 Published:2017-01-16

摘要: 为了获得飞行器高精度、高稳定性的定位结果, 结合低成本的单目摄像机和惯性测量单元, 通过对单目视觉和惯性测量进行多传感器融合, 实现飞行器的自定位。 视觉定位模块中, 对 3 种当前最新的单目视觉定位方法分别进行不同场景下的实验和比较, 分析它们各自用于飞行器定位时的优势和劣势。 为了解决视觉定位对图像特征的依赖问题, 引入一种基于扩展卡尔曼滤波的多传感器融合方法, 将视觉定位和惯性测量以松耦合方式进行融合, 用惯性测量补偿因视觉定位不稳定而产生的误差。 实验证明, 该方法在视觉定位不稳定的情况下能有效降低定位结果的误差。

关键词: 飞行器, 惯性测量, 单目视觉, 多传感器融合, 自定位

Abstract:  In order to aquire accurate and robust positioning results, a low cost monocular camera and an IMU(Inertial Measurement Unit) are combined by sensor fusion for the ego-positioning of MAVs (Micro Air Vehicles). In the visual positioning module, three of the most representative state-of-the-art monocular visual positioning approaches, which are originally designed for vehicles, are evaluated in different conditions. Based on the experiment results, the pros and cons of each method are analyzed and discussed. Considering the limitations of vision-2only approaches, an EKF (Extended Kalman Filter) is used to fuse the visual positioning with an inertial sensor based on a loosely-coupled framework, to compensate for the instability of vision. The experiment results demonstrate that the visual-inertial sensor fusion can effectively reduce the positioning errors in unstable visual cases.

Key words: monocular vision, sensor fusion, inertial measurement, micro air vehicles(MAVs),  positioning

中图分类号: 

  • TP391