for simultaneous localization and mapping (SLAM) specifically designed for the Velodyne HDL-64E laser scanner which exhibits characteristics not present in most other systems. This comprises the continuous, spinning data acquisition and the relative high sensor noise. Together, these make standard SLAM approaches

scanner SLAM. For the stereo case, this is however impractical due to the high noise of stereo reconstructed point clouds. In this paper we propose a dense stereo V-SLAM algorithm that estimates a dense 3D map representation which is more accurate than raw stereo measurements. Thereto, we run a sparse V-SLAM

Abstract — Automatic sensing of the environment is a fundamental scientific issue in robotics, since it is essential for autonomous mobile robot systems. In previous works, we presented a 6D SLAM algorithm which is based on the spatial data from the AIS 3D Laser Scanner and a variant

algorithm we have recently developed for our needs in autonomous automotive applications. Our approach has the particularity of making use exclusively of laser scanners to achieve our goals without using any other type of sensors or source of information. We concentrated on developing a self

A new solution for the SLAM problem is presented which makes use of a scan matching algorithm, and does not rely on bayesian filters. The virtual map is represented in the form of an occupancy grid, which stores laser scans based on the estimated position. The occupancy grid is scanned by means

for the tasks of stereo, optical flow, visual odometry / SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual

presented a global optimization from the mechatronic to the algorithmic level. In this article, we present a solution to the SLAM problem based on the co-design of a slim rotating distance scanner, a lightweight SLAM software, and an optimization methodology. The scanner consists of a set of infrared

Abstract — This paper presents the extension and experimental validation of the widely used EKF 1-based SLAM 2 algorithm to 3D space. It uses planar features extracted probabilistically from dense three-dimensional point clouds generated by a rotating 2D laser scanner. These features

Abstract — In this paper a new feature representation for Simultaneous Localization and Mapping (SLAM) is discussed. The representation addresses feature symmetries and constraints explicitly to make the basic model numerically robust. In previous SLAM work, complete initialization of features

Computer vision approaches are increasingly used in mobile robotic systems, since they allow to obtain a very good representation of the environment by using low-power and cheap sensors. In particular it has been shown that they can compete with standard solutions based on laser range scanners when