In this paper, we propose a survey of the Simultaneous Localization And Map** (SLAM)
field when considering the recent evolution of autonomous driving. The growing interest …

Autonomous vehicles promise numerous improvements to vehicular traffic: an increase in
both highway capacity and traffic flow because of faster response times, less fuel …

Localization and reconstruction are two highly related research areas. Both of them have
developed rapidly in recent years. Apparently, with the help of ground and aerial meta-data …

In this paper, we investigate the problem of visual odometry for ground vehicles based on
the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed …

In this paper, we propose the Road-SLAM algorithm, which robustly exploits road markings
obtained from camera images. Road markings are well categorized and informative but …

We present a framework for global vehicle localization and 3D point cloud reconstruction
that combines stereo visual odometry, satellite images, and road maps under a particle …

Obtaining an accurate vehicle position is important for intelligent vehicles in supporting
driver safety and comfort. This paper proposes an accurate ego-localization method by …

Accurate and reliable localization in urban environments is critical for high-level
autonomous driving systems. In comparison to other localization strategies, map-based …

Maps are needed for a wide range of applications. In the context of mobile robotics, the map
learning problem under uncertainty is often referred to as the simultaneous localization and …

In this letter, we propose a method for accurately estimating the 6-Degree Of Freedom (DOF)
pose in an urban environment when a High Definition (HD) map is available. An HD map …