scholarly journals LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching

2021 ◽  
Vol 13 (22) ◽  
pp. 4618
Author(s):  
Xupei Zhang ◽  
Zhanzhuang He ◽  
Zhong Ma ◽  
Zhongxi Wang ◽  
Li Wang

Local features extraction is a crucial technology for image matching navigation of an unmanned aerial vehicle (UAV), where it aims to accurately and robustly match a real-time image and a geo-referenced image to obtain the position update information of the UAV. However, it is a challenging task due to the inconsistent image capture conditions, which will lead to extreme appearance changes, especially the different imaging principle between an infrared image and RGB image. In addition, the sparsity and labeling complexity of existing public datasets hinder the development of learning-based methods in this research area. This paper proposes a novel learning local features extraction method, which uses local features extracted by deep neural network to find the correspondence features on the satellite RGB reference image and real-time infrared image. First, we propose a single convolution neural network that simultaneously extracts dense local features and their corresponding descriptors. This network combines the advantages of a high repeatability local feature detector and high reliability local feature descriptors to match the reference image and real-time image with extreme appearance changes. Second, to make full use of the sparse dataset, an iterative training scheme is proposed to automatically generate the high-quality corresponding features for algorithm training. During the scheme, the dense correspondences are automatically extracted, and the geometric constraints are added to continuously improve the quality of them. With these improvements, the proposed method achieves state-of-the-art performance for infrared aerial (UAV captured) image and satellite reference image, which shows 4–6% performance improvements in precision, recall, and F1-score, compared to the other methods. Moreover, the applied experiment results show its potential and effectiveness on localization for UAVs navigation and trajectory reconstruction application.

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 198 ◽  
Author(s):  
Zilong Deng ◽  
Dongxiao Yang ◽  
Xiaohu Zhang ◽  
Yuguang Dong ◽  
Chengbo Liu ◽  
...  

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.


2019 ◽  
Vol 56 (9) ◽  
pp. 091003
Author(s):  
谭光鸿 Tan Guanghong ◽  
侯进 Hou Jin ◽  
韩雁鹏 Han Yanpeng ◽  
罗朔 Luo Shuo

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