Campus Units
Electrical and Computer Engineering
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
9-1-2020
Journal or Book Title
IEEE Transactions on Neural Networks and Learning Systems
DOI
10.1109/TNNLS.2020.3015660
Abstract
Matching and registration between aerial images and prestored road landmarks are critical techniques to enhance unmanned aerial system (UAS) navigation in the global positioning system (GPS)-denied urban environments. Current registration processes typically consist of two separate stages of road extraction and road registration. These two-stage registration approaches are time-consuming and less robust to noise. To that end, in this article, we, for the first time, investigate the problem of end-to-end Aerial-Road registration. Using deep learning, we develop a novel attention-based neural network architecture for Aerial-Road registration. In this model, we construct two-branch neural networks with shared weights to map two input images into a common embedding space. Besides, considering that road features are sparsely distributed in images, we incorporate a novel multibranch attention module to filter out false descriptor matches from the indiscriminative background in order to improve registration accuracy. Finally, the results from extensive experiments show that compared with state-of-the-art approaches, the mean absolute errors of our approach in rotation angle and the translations in the x- and y-directions are reduced down by a factor of 1.24, 1.38, and 1.44, respectively. Furthermore, as a byproduct, our experimental results prove the feasibility of a neural network multitask learning approach to simultaneously achieve accurate Aerial-Road matching and registration, thus providing an efficient and accurate UAS geolocalization.
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright Owner
IEEE
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Wang, Teng; Zhao, Ye; Wang, Jiawei; Somani, Arun K.; and Sun, Changyin, "Attention-Based Road Registration for GPS-Denied UAS Navigation" (2020). Electrical and Computer Engineering Publications. 286.
https://lib.dr.iastate.edu/ece_pubs/286
Included in
Artificial Intelligence and Robotics Commons, Navigation, Guidance, Control and Dynamics Commons, Systems and Communications Commons
Comments
This is a manuscript of an article published as Wang, Teng, Ye Zhao, Jiawei Wang, Arun K. Somani, and Changyin Sun. "Attention-based road registration for GPS-denied UAS navigation." IEEE Transactions on Neural Networks and Learning Systems (2020). DOI: 10.1109/TNNLS.2020.3015660. Posted with permission.