Abstract Simultaneous Localization and Map** (SLAM) is a process to use multiple
sensors to position an unmanned mobile vehicle without previous knowledge of the …

In the context of automated driving, navigating through challenging urban environments with
dynamic objects, large-scale scenes, and varying lighting/weather conditions, achieving …

This article presents ORB-SLAM3, the first system able to perform visual, visual-inertial and
multimap SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye …

Where am I? This is one of the most critical questions that any intelligent system should
answer to decide whether it navigates to a previously visited area. This problem has long …

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 …

We present an approach to indoor scene reconstruction from RGB-D video. The key idea is
to combine geometric registration of scene fragments with robust global optimization based …

Dense 3D reconstruction and ego-motion estimation are key challenges in autonomous
driving and robotics. Compared to the complex, multi-modal systems deployed today, multi …

Visual SLAM (simultaneous localization and map**) refers to the problem of using
images, as the only source of external information, in order to establish the position of a …

Visual odometry (VO) is the process of estimating the egomotion of an agent (eg, vehicle,
human, and robot) using only the input of a single or multiple cameras attached to it …

RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB
images along with per-pixel depth information. In this paper we investigate how such …