In cooperative, connected, and automated mobility (CCAM), the more automated vehicles
can perceive, model, and analyze the surrounding environment, the more they become …

Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative
perception, planning, and action operations. One of the foundational layers of such stacks is …

Precise localization is essential but also challenging for autonomous vehicles. In this article,
A novel visual localization method is proposed. Specifically, a semantic local map …

Monocular Re-Localization (MRL) is an essential component in autonomous applications,
estimating 6 degree-of-freedom ego poses wrt the scene map using monocular images …

Vision-based map-matching with HD map for high precision vehicle localization has gained
great attention for its low-cost and ease of deployment. However, its localization …

Robust and accurate localization is an essential component for robotic navigation and
autonomous driving. The use of cameras for localization with high definition map (HD Map) …

Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD
maps is highly relevant for the current advances in autonomous driving, but remains a …

Binary feature descriptors have been widely used in various visual measurement tasks,
particularly those with limited computing resources and storage capacities. Existing binary …

Accurate localization ability is fundamental in autonomous driving. Traditional visual
localization frameworks approach the semantic map-matching problem with geometric …