  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.
