Visual Localization with Offline Google Satellite Map-Assisted for Ground Vehicles in GNSS-Denied Environment

Abstract

Vehicle localization is a critical component in the planning and navigation of autonomous driving system. Generally, traditional vehicle localization methods rely on the Global Navigation Satellite System (GNSS) for self-localization. Unfortunately, GNSS can become unreliable and may fail in urban canyons, under trees, and beneath overpasses. To address this problem, we propose a visual localization framework assisted by offline Google satellite maps in GNSS-weak or GNSS-denied environments. And we introduce learning-based ground-to-satellite map feature matching method to mitigate the long-term cumulative drift of visual odometry. To reduce the negative impact of cross-view matching errors on localization accuracy, we propose a novel cross-view pose selection method to build two pose uncertainty models. Moreover, we combine the proposed method with classical SLAM methods to develop a vehicle localization framework. To verify the performance of the proposed method, we carried out the accuracy comparison experiment with state-of-the-art fusion localization methods and feature matching methods. Experimental results indicate that the proposed method achieves the best localization performance compared with the state-of-the-art methods, and our method achieves the root mean square error of 0.290m and 0.014rad in KITTI-05. The implementation code of this paper will be open-source at https://github.com/NEU-REAL/visualLocalization-with-satelliteMap.