利用无人机IMU数据的航拍图像拼接
Aerial Image Stitching Using IMU Data from a UAV
AI总结：
本文提出一种结合IMU数据与计算机视觉的无人机航拍图像拼接方法，通过估计位移旋转、校正透视畸变并计算单应性矩阵，在精度和鲁棒性上优于现有基于特征的拼接算法。
摘要：
无人飞行器（UAV）被广泛用于航空摄影和遥感应用。其中一个主要挑战是将多幅图像拼接成一幅覆盖大范围区域的单一高分辨率图像。基于特征的图像拼接算法被普遍使用，但在特征检测和匹配中可能出现错误和歧义。为解决这一问题，已提出若干方法，包括使用光束法平差技术或直接图像对齐。本文提出一种新方法，结合IMU数据与计算机视觉技术对无人机拍摄的图像进行拼接。该方法包括若干步骤，如估计无人机在连续图像之间的位移和旋转、校正透视畸变以及计算单应性矩阵。随后我们使用标准图像拼接算法对图像进行对齐和融合。所提方法利用了IMU数据提供的额外信息，校正多种畸变来源，并且可以容易地集成到现有无人机工作流程中。实验证明了该方法的有效性和鲁棒性，在精度和可靠性方面优于一些现有的基于特征的图像拼接算法，尤其是在大位移、大旋转和相机姿态变化等具有挑战性的场景中。
英文摘要：
Unmanned Aerial Vehicles (UAVs) are widely used for aerial photography and remote sensing applications. One of the main challenges is to stitch together multiple images into a single high-resolution image that covers a large area. Featurebased image stitching algorithms are commonly used but can suffer from errors and ambiguities in feature detection and matching. To address this, several approaches have been proposed, including using bundle adjustment techniques or direct image alignment. In this paper, we present a novel method that uses a combination of IMU data and computer vision techniques for stitching images captured by a UAV. Our method involves several steps such as estimating the displacement and rotation of the UAV between consecutive images, correcting for perspective distortion, and computing a homography matrix. We then use a standard image stitching algorithm to align and blend the images together. Our proposed method leverages the additional information provided by the IMU data, corrects for various sources of distortion, and can be easily integrated into existing UAV workflows. Our experiments demonstrate the effectiveness and robustness of our method, outperforming some of the existing feature-based image stitching algorithms in terms of accuracy and reliability, particularly in challenging scenarios such as large displacements, rotations, and variations in camera pose.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
