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 …

Loop closure detection is of vital importance in the process of simultaneous localization and
mapping (SLAM), as it helps to reduce the cumulative error of the robot's estimated pose and …

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 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 …

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 …

This paper documents a large scale, long-term autonomy dataset for robotics research
collected on the University of Michigan's North Campus. The dataset consists of …

For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has
to deal with unknown initial positioning caused by either the kidnapped robot problem or …

In this paper we present for the first time a relocalisation method for keyframe-based SLAM
that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of …

Many estimation, planning, and optimal control problems in robotics have an optimization
problem at their core. In most of these optimization problems, the objective to be maximized …