This study investigates the difficulties associated with image registration due to variations in perspective, lighting, and ground object details between images captured by drones and satellite imagery. This study proposes an image registration and drone visual localization algorithm based on an attention mechanism. Initially, an improved Oriented FAST and Rotated BRIEF (ORB) algorithm incorporating a quadtree-based feature point homogenization method is designed to extract image feature points, providing support for the initial motion estimation of UAVs. Following this, we combined a convolutional neural network with an attention mechanism and the inverse-combined Lucas-Kanade method to further extract image features. This integration facilitates the efficient registration of drone images with satellite tiles. Finally, we utilized the registration results to correct the initial motion of the drone and accurately determine its location. Our experimental findings indicate that the proposed algorithm achieves an average absolute positioning error of less than 40 m for low-texture flight paths and under 10 m for high-texture paths. This significantly mitigates the positioning challenges that arise from inconsistencies between drone images and satellite maps. Moreover, our method demonstrates a notable improvement in computational speed compared to existing algorithms.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    UAVs-Based Visual Localization via Attention-Driven Image Registration Across Varying Texture Levels


    Beteiligte:
    Yan Ren (Autor:in) / Guohai Dong (Autor:in) / Tianbo Zhang (Autor:in) / Meng Zhang (Autor:in) / Xinyu Chen (Autor:in) / Mingliang Xue (Autor:in)


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Visual attention based target detection and tracking for UAVs

    Tingting Wang / Yu Zhang / Zhihao Cai et al. | IEEE | 2016


    Self-localization of UAVs based on visual-inertial integrated navigation

    Zheng, Jinghao / Lei, Bo / Tan, Hai | SPIE | 2024


    Dynamic output feedback image-based visual servoing of rotorcraft UAVs

    Xie, Hui / Li, Jianan / Low, K.H. | IEEE | 2017


    Optimal Attention Allocation to Visual Search Tasks of Multi-UAVs Based on Operator Model

    Jian, Lixuan / Yin, Dong / Shen, Lincheng et al. | British Library Conference Proceedings | 2015


    Image registration using multi-scale texture moments

    Sato, J. / Cipolla, R. | British Library Online Contents | 1995