Range–Visual–Inertial Odometry with Coarse-to-Fine Image Registration Fusion for UAV Localization
Author:
Hao Yun1ORCID, He Mengfan1, Liu Yuzhen2ORCID, Liu Jiacheng3ORCID, Meng Ziyang1
Affiliation:
1. Department of Precision Instrument, Tsinghua University, Beijing 100084, China 2. Robotics X, Tencent, Shenzhen 518057, China 3. Unmanned Aerial Vehicle Lab, Meituan, Beijing 100012, China
Abstract
In Global Navigation Satellite System (GNSS)-denied environments, image registration has emerged as a prominent approach to utilize visual information for estimating the position of Unmanned Aerial Vehicles (UAVs). However, traditional image-registration-based localization methods encounter limitations, such as strong dependence on the prior initial position information. In this paper, we propose a systematic method for UAV geo-localization. In particular, an efficient range–visual–inertial odometry (RVIO) is proposed to provide local tracking, which utilizes measurements from a 1D Laser Range Finder (LRF) to suppress scale drift in the odometry. To overcome the differences in seasons, lighting conditions, and other factors between satellite and UAV images, we propose an image-registration-based geo-localization method in a coarse-to-fine manner that utilizes the powerful representation ability of Convolutional Neural Networks (CNNs). Furthermore, to ensure the accuracy of global optimization, we propose an adaptive weight assignment method based on the evaluation of the quality of image-registration-based localization. The proposed method is extensively evaluated in both synthetic and real-world environments. The results demonstrate that the proposed method achieves global drift-free estimation, enabling UAVs to accurately localize themselves in GNSS-denied environments.
Funder
Beijing Natural Science Foundation National Natural Science Foundation of China
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
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