We review the use of factor graphs for the modeling and solving of large-scale inference
problems in robotics. Factor graphs are a family of probabilistic graphical models, other …

Autonomous underwater vehicle (AUV) navigation and localization in underwater
environments is particularly challenging due to the rapid attenuation of Global Positioning …

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 …

Visual place recognition is a challenging problem due to the vast range of ways in which the
appearance of real-world places can vary. In recent years, improvements in visual sensing …

In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We
recorded a large set of image sequences from a Microsoft Kinect with highly accurate and …

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing
progress over the last few decades and has generated considerable interest in the …

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

This paper is intended to pave the way for new researchers in the field of robotics and
autonomous systems, particularly those who are interested in robot localization and …

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation
enabling a better understanding of existing graphical model inference algorithms and their …

Many popular problems in robotics and computer vision including various types of
simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased …