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 …

Automated driving systems are in need of accurate localization, ie, achieving accuracies
below 0.1 m at confidence levels above 95%. Although during the last decade numerous …

Using geometric landmarks like lines and planes can increase navigation accuracy and
decrease map storage requirements compared to commonly-used LiDAR point cloud maps …

Cooperative Simultaneous Localization and Mapping (C-SLAM) is an active research topic
in mobile robotics. However, its application in the field of autonomous driving is rare. While …

Accurate localization for autonomous vehicle operations is essential in dense urban areas.
In order to ensure safety, positioning algorithms should implement fault detection and …

Using pole and plane objects in lidar SLAM can increase accuracy and decrease map
storage requirements compared to commonly-used point cloud maps. However, place …

This paper presents a novel GNSS-free semantic LiDAR localization algorithm for
autonomous vehicles. It uses standardized HD and open-source maps to ensure localization …

Simultaneous localization and mapping (SLAM) is an essential technique for autonomous
driving. Recently, combining image recognition technology to generate semantically …

Localisation has become a major need for the routing and processing of data collected by
wireless sensor network. However, equipping all network nodes with GPS is not obvious …

Simultaneous localization and mapping is a prevalent method for localization in automated
parking applications. However, it requires to access an area for exploring purposes before …