Vision-based Online Localization and Trajectory Smoothing for Fixed-wing UAV Tracking a Moving Target

被引：8
作者：
Zhou, Yong [1 ]
Tang, Dengqing [1 ]
Zhou, Han [1 ]
Xiang, Xiaojia [1 ]
Hu, Tianjiang [2 ]
机构：
[1] Natl Univ Def Technol, Changsha, Peoples R China
[2] Sun Yat Sen Univ, Guangzhou, Peoples R China
来源：
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) | 2019年
关键词：
D O I：
10.1109/ICCVW.2019.00024
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Using unmanned aerial vehicle (UAV) to estimate and predict the position and motion of the ground target has been widely focused on in many computer vision tasks. This work aims to address the development of a vision-based ground target localization and estimation for a fixed-wing UAV. Limited by the lightweight onboard processor, it is conflicting with the need for online onboard operation and the computing resource limitation of the platform. In this paper, we develop a practical approach to recover dynamic targets based on extended Kalman filter (EKF) for localization and locally weighted regression for trajectory smoothing. Our methods run online in real time with the only data up to the current timestep. The flight experiment results show the effective tracking and localization of the ground moving target.
引用
收藏
页码：153 / 160
页数：8
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