MLP-Based Efficient Stitching Method for UAV Images
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS(2022)
Abstract
Unmanned aerial vehicle (UAV) image stitching techniques based on position and attitude information have shown clear speed superiority over feature-based counterparts. However, how to improve stitching accuracy and robustness remains a great challenge since position and attitude parameters are sensitive to noise introduced by sensors and external environment. To mitigate this issue, this work presents a simple yet effective stitching algorithm for UAV images based on a coarse-to-fine strategy. Specifically, we first conduct coarse registration using the position and attitude information obtained from GPS, IMU, and altimeter. Then, we introduce a novel offline calibration phase that is designed to regress the obtained global transformation matrix to the optimal one computed from feature-based algorithms, by using multi-layer perceptron (MLP) neural networks for fast correction. Consequently, the proposed method well integrates the complementary strengths of both parameter and feature-based methods, achieving an ideal speed–accuracy tradeoff. Moreover, to facilitate research on this topic, we establish a new dataset, named UAV-AIRPAI, that comprises over 100 UAV image pairs with position and attitude annotations to the community, opening up a promising direction for UAV image stitching. Extensive experiments on the UAV-AIRPAI dataset show that our method achieves superior accuracy compared to priors while running at a real-time speed of 0.0124 s per image pair. Code and data will be available at
https://github.com/dededust/UAV-AIRPAI
.
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Key words
Autonomous aerial vehicles, Image stitching, Training, Real-time systems, Image registration, Cameras, Annotations, Aerial image, image registration, multi-layer perceptron (MLP), position, and attitude
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