The use of aerial swarms to solve real-world problems has been increasing steadily,
accompanied by falling prices and improving performance of communication, sensing, and …

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 …

Multi-robot simultaneous localization and mapping (SLAM) is a crucial capability to obtain
timely situational awareness over large areas. Real-world applications demand multi-robot …

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 …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

To achieve collaborative tasks, robots in a team need to have a shared understanding of the
environment and their location within it. Distributed Simultaneous Localization and Mapping …

Place recognition in 3D data is a challenging task that has been commonly approached by
adapting image-based solutions. Methods based on local features suffer from ambiguity 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 …

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 …

Simultaneous localization and mapping (SLAM) in unknown GPS‐denied environments is a
major challenge for researchers in the field of mobile robotics. Many solutions for single …