Review of multi-modal image matching assisted inertial navigation positioning technology for unmanned aerial vehicle

被引：0
|
作者：
Luo S. [1 ,2 ]
Liu H. [1 ]
Hu M. [2 ]
Dong J. [3 ]
机构：
[1] School of Automation, Central South University, Changsha
[2] School of Aeronautics and Astronautics, Central South University, Changsha
[3] College of Aerospace Science and Engineering, National University of Defense Technology, Changsha
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2020年 / 42卷 / 06期
关键词：
Aircraft positioning; Image matching; Integrated navigation; Multi-modal image;
D O I：
10.11887/j.cn.202006001
中图分类号：
学科分类号：
摘要：
High-precision positioning and navigation in GPS(global positioning system) denied environments is a key technology for aircraft achieving autonomous scout, cruise, and strike. Vision navigation has the advantages of passive-type, low cost, and avoidable accumulated errors avoidable, etc. The fusion of vision and inertial navigation can give full play to their advantages and achieve the purpose of high-precision positioning. Firstly, the development of aircraft positioning technology based on multi-modal image matching assisted inertial navigation was summarized. Then this technology was elaborated in five aspects: the vision-internal calibration, multi-modal image matching, attitude algorithm, data fusion, and back-end optimization. Finally, four possible future directions were proposed as follows, two types of passive positioning combined navigation systems based on deep learning, multi-modal image matching and inertial navigation. The four possible future directions provide a reference for realizing multi-modal image matching assisted inertial navigation aircraft positioning technology. © 2020, NUDT Press. All right reserved.
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页码：1 / 10
页数：9
相关论文
共 75 条
  • [1] Gianpaolo C, Patrick D., An integrated UAV navigation system based on aerial image matching, Proceedings of IEEE Aerospace Conference, pp. 1-10, (2008)
  • [2] Mostafa K, Majid B, Ali P., UAV navigation based on PIIFD/INS method, International Journal of Computer Theory and Engineering, 4, 2, pp. 283-287, (2012)
  • [3] Zhang J, Liu W S, Wu Y R., Novel technique for vision-based UAV navigation, IEEE Transactions on Aerospace and Electronic Systems, 47, 4, pp. 2731-2741, (2011)
  • [4] Liu H Q, Luo S B, Lu J Z., Method for adaptive robust four-wheel localization and application in automatic parking systems, IEEE Sensors Journal, 19, 22, pp. 10644-10653, (2019)
  • [5] Cesetti A, Frontoni E, Mancini A, Et al., A vision-based guidance system for UAV navigation and safe landing using natural landmarks, Journal of Intelligent & Robotic Systems, 57, 1, pp. 233-257, (2010)
  • [6] LI Yaojun, PAN Quan, ZHAO Chunhui, Et al., Scene matching navigation for UAV based on spatial relationship geometric constraints, Application Research of Computers, 27, 10, pp. 3822-3825, (2010)
  • [7] LI Yaojun, PAN Quan, ZHAO Chunhui, Et al., Natural-landmark scene matching vision navigation based on dynamic keyframe, Opto-Electronic Engineering, 37, 9, pp. 32-38, (2010)
  • [8] David L., Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150-1157, (1999)
  • [9] David L., Distinctive image features from scale-invariant key points, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)
  • [10] David L., Local feature view clustering for 3D object recognition, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1, pp. 682-688, (2001)
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