  Homography estimation between multiple aerial images can provide relative
pose estimation for collaborative autonomous exploration and monitoring. The
usage on a robotic system requires a fast and robust homography estimation
algorithm. In this study, we propose an unsupervised learning algorithm that
trains a Deep Convolutional Neural Network to estimate planar homographies. We
compare the proposed algorithm to traditional feature-based and direct methods,
as well as a corresponding supervised learning algorithm. Our empirical results
demonstrate that compared to traditional approaches, the unsupervised algorithm
achieves faster inference speed, while maintaining comparable or better
accuracy and robustness to illumination variation. In addition, on both a
synthetic dataset and representative real-world aerial dataset, our
unsupervised method has superior adaptability and performance compared to the
supervised deep learning method.
