Simultaneous localization and map** (SLAM) consists in the concurrent construction of a
model of the environment (the map), and the estimation of the state of the robot moving …

We propose an information-theoretic planning approach that enables mobile robots to
autonomously construct dense 3D maps in a computationally efficient manner. Inspired by …

The high-precision localization module is an essential component of unmanned underwater
vehicles (UUVs), providing critical parameter information for motion control, decision …

We investigate the problem of planning under uncertainty, with application to mobile
robotics. We propose a probabilistic framework in which the robot bases its decisions on the …

This paper reports on an integrated navigation algorithm for the visual simultaneous
localization and map** (SLAM) robotic area coverage problem. In the robotic area …

It is common to implicitly assume access to intelligently captured inputs (eg, photos from a
human photographer), yet autonomously capturing good observations is itself a major …

We present a novel method for planning coverage paths for inspecting complex structures
on the ocean floor using an autonomous underwater vehicle (AUV). Our method initially …

In this paper we examine the problem of autonomously exploring and map** an
environment using a mobile robot. The robot uses a graph-based SLAM system to perform …

In this paper, we formulate the active SLAM paradigm in terms of model-free Deep
Reinforcement Learning, embedding the traditional utility functions based on the Theory of …

The purpose of this work is to highlight the paramount importance of representing and
quantifying uncertainty to correctly report the associated confidence of the robot's location …