Local to Global: Efficient Visual Localization for a Monocular Camera

Abstract

Robust and accurate visual localization is one of the most fundamental elements in various technologies, such as autonomous driving and augmented reality. While recent visual localization algorithms demonstrate promising results in terms of accuracy and robustness, the associated high computational cost requires running these algorithms on server-sides rather than client devices. This paper proposes a real time monocular visual localization system that combines client-side visual odometry with server-side visual localization functionality. In particular, the proposed system utilizes handcrafted features for real time visual odometry while adopting learned features for robust visual localization. To link the two components, the proposed system employs a map alignment mechanism that transforms the local coordinates obtained using visual odometry to global coordinates. The system achieves comparable accuracy to that of the state-of-the-art structure-based methods and end-to-end methods for the visual localization on both indoor and outdoor datasets while operating in real time.