Autonomous navigation requires both a precise and robust mapping and localization
solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well …

Bayesian inference provides a consistent method for the extraction of information from
physics experiments even in ill-conditioned circumstances. The approach provides a unified …

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

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a
model of the environment (the map), and the estimation of the state of the robot moving …

This paper investigates the convergence properties and consistency of extended Kalman
filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of …

We present a system for monocular simultaneous localization and mapping (mono-SLAM)
relying solely on video input. Our algorithm makes it possible to precisely estimate the …

Iterative Closest Point (ICP) is a widely used method for performing scan-matching and
registration. Being simple and robust, this method is still computationally expensive and may …

This paper introduces a new approach to simultaneous localization and mapping (SLAM)
that pursues robustness and accuracy in large-scale environments. Like most successful …

Simultaneous localization and mapping (SLAM) algorithms based on local maps have been
demonstrated to be well suited for mapping large environments as they reduce the …

This paper describes a geometrically constrained Extended Kalman Filter (EKF) framework
for a line feature based SLAM, which is applicable to a rectangular indoor environment. Its …