The Motion Estimation of Unmanned Aerial Vehicle Axial Velocity Using Blurred Images
Author:
Mao Yedong1, Zhan Quanxi2, Yang Linchuan2ORCID, Zhang Chunhui1, Xu Ge1, Shen Runjie2ORCID
Affiliation:
1. Technology & Research Center, China Yangtze Power Co., Ltd., Yichang 443002, China 2. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Abstract
This study proposes a novel method for estimating the axial velocity of unmanned aerial vehicles (UAVs) using motion blur images captured in environments where GPS signals are unavailable and lighting conditions are poor, such as underground tunnels and corridors. By correlating the length of motion blur observed in images with the UAV’s axial speed, the method addresses the limitations of traditional techniques in these challenging scenarios. We enhanced the accuracy by synthesizing motion blur images from neighboring frames, which is particularly effective at low speeds where single-frame blur is minimal. Six flight experiments conducted in the corridor of a hydropower station demonstrated the effectiveness of our approach, achieving a mean velocity error of 0.065 m/s compared to ultra-wideband (UWB) measurements and a root-mean-squared error within 0.3 m/s. The results highlight the stability and precision of the proposed velocity estimation algorithm in confined and low-light environments.
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