Aerial Image Stitching Using IMU Data from a UAV

TL;DR

This paper introduces a UAV image stitching method that combines IMU data with computer vision to improve accuracy and robustness in challenging scenarios involving large displacements and rotations.

Contribution

A novel UAV image stitching approach that integrates IMU data with vision techniques to enhance accuracy and robustness over existing feature-based methods.

Findings

Outperforms existing algorithms in accuracy and reliability.

Effective in large displacements and rotations.

Robust against camera pose variations.

Abstract

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.