Deep Learning-Based UAV Aerial Triangulation without Image Control Points

TL;DR

This paper introduces a deep learning-based UAV aerial triangulation method that improves image registration accuracy without the need for control points, enhancing large-scale drone mapping efficiency.

Contribution

It proposes a novel UAV image registration approach using deep learning features, specifically SuperPoint, to achieve high-precision aerial triangulation without control points.

Findings

Outperforms traditional SIFT-based methods in accuracy and efficiency

Achieves high-precision triangulation suitable for large-scale UAV surveys

Demonstrates robustness against image distortions and environmental factors

Abstract

The emerging drone aerial survey has the advantages of low cost, high efficiency, and flexible use. However, UAVs are often equipped with cheap POS systems and non-measurement cameras, and their flight attitudes are easily affected. How to realize the large-scale mapping of UAV image-free control supported by POS faces many technical problems. The most basic and important core technology is how to accurately realize the absolute orientation of images through advanced aerial triangulation technology. In traditional aerial triangulation, image matching algorithms are constrained to varying degrees by preset prior knowledge. In recent years, deep learning has developed rapidly in the field of photogrammetric computer vision. It has surpassed the performance of traditional handcrafted features in many aspects. It has shown stronger stability in image-based navigation and positioning tasks, especially it has better resistance to unfavorable factors such as blur, illumination changes, and geometric distortion. Based on the introduction of the key technologies of aerial triangulation without image control points, this paper proposes a new drone image registration method based on deep learning image features to solve the problem of high mismatch rate in traditional methods. It adopts SuperPoint as the feature detector, uses the superior generalization performance of CNN to extract precise feature points from the UAV image, thereby achieving high-precision aerial triangulation. Experimental results show that under the same pre-processing and post-processing conditions, compared with the traditional method based on the SIFT algorithm, this method achieves suitable precision more efficiently, which can meet the requirements of UAV aerial triangulation without image control points in large-scale surveys.