Open AccessAccurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging
Author(s) -
Xie Xiuchuan,
Yang Tao,
Zhang Yanning,
Liang Bang,
Liu Linfeng
Publication year - 2022
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/cvi2.12053
Subject(s) - computer vision , computer science , artificial intelligence , robustness (evolution) , position (finance) , motion estimation , relative motion , biochemistry , chemistry , physics , finance , mechanics , economics , gene
Small unmanned aerial vehicles (UAVs) have developed rapidly and are widely used for disaster relief, traffic monitoring and military surveillance. To perform these tasks better, it is necessary to improve the environmental perception ability of UAVs in a dynamic environment, including their static and dynamic perception ability. Specifically, both three‐dimensional reconstruction for a static scene and localization for moving objects are required. Simultaneous Localization And Mapping technology has made great progress in static scene structure reconstruction and UAV self‐motion estimation. However, accurate real‐time localization of moving objects is still challenging. In this article, a global averaging based localization method is proposed to locate moving objects for a small UAV platform. Inspired by global structure from motion, this idea is applied to the localization of moving objects. To solve moving object localization, the relative motion estimation and global position optimisation methods are proposed. The proposed method was tested in various scenarios with a several trajectories. The extensive experimental results demonstrate the robustness and effectiveness of the proposed method.