The Motion Estimation of Unmanned Aerial Vehicle Axial Velocity Using Blurred Images

• Published in: Drones (Basel) Vol. 8; no. 7; p. 306
• Main Authors: Mao, Yedong, Zhan, Quanxi, Yang, Linchuan, Zhang, Chunhui, Xu, Ge, Shen, Runjie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; Algorithms; axial velocity estimation; Blurring; Cameras; corridor; Drone aircraft; Effectiveness; Error analysis; Estimation; Global Positioning System; Hydroelectric power stations; Image enhancement; Image processing; Methods; motion blur image; Motion simulation; Neural networks; poor-light scene; Sensors; Signal processing; UAV; Ultrawideband; Unmanned aerial vehicles; Velocity; Velocity errors; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8070306

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.