Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
This addresses the need for efficient relative pose estimation in collaborative autonomous exploration and monitoring for robotic systems, representing an incremental improvement over existing deep learning approaches.
The paper tackled the problem of fast and robust homography estimation for aerial images in robotics by proposing an unsupervised deep learning algorithm, which achieved faster inference speed while maintaining comparable or better accuracy and robustness to illumination variation compared to traditional and supervised methods.
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.