Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

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 …

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 …

This paper presents a visual-inertial odometry framework that tightly fuses inertial
measurements with visual data from one or more cameras, by means of an iterated …

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly
using pixel intensity errors of image patches, achieves accurate tracking performance while …

In the last decade, the research on and the technology for outdoor tracking have seen an
explosion of advances. It is expected that in the near future, we will witness similar trends for …

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 …

This paper describes the simultaneous localization and map** (SLAM) problem and the
essential methods for solving the SLAM problem and summarizes key implementations and …

While the most accurate solution to off-line structure from motion (SFM) problems is
undoubtedly to extract as much correspondence information as possible and perform batch …