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 a network of robots working in a specific environment, relative localization among robots
is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of …

Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for
multi-robot applications in environments where absolute positioning is not available. Being …

We propose a novel framework for distributed, multi-robot SLAM intended for use with 3D
LiDAR observations. The framework, DiSCo-SLAM, is the first to use the lightweight Scan …

To execute collaborative tasks in unknown environments, a robotic swarm must establish a
global reference frame and locate itself in a shared understanding of the environment …

To improve the accuracy and efficiency of 3D LiDAR mapping, real-time cooperative SLAM
has been considered to explore large and complex areas. To merge the individual maps …

This paper provides a fully decentralized algorithm for collaborative localization based on
the extended Kalman filter. The major challenge in decentralized collaborative localization is …

Collaborative SLAM is at the core of perception in multi-robot systems as it enables the co-
localization of the team of robots in a common reference frame, which is of vital importance …

Distributed estimation of 6-DOF relative states, including three-dimensional relative poses
and three-dimensional relative positions, is a key problem in UAV (Unmanned Aerial …

There is a growing interest on formal methods-based robotic planning for temporal logic
objectives. In this work, we extend the scope of existing synthesis methods to hyper …