Active simultaneous localization and map** (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

This article presents a comprehensive review of the Active Simultaneous Localization and
Map** (A-SLAM) research conducted over the past decade. It explores the formulation …

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 …

A significant growth in Unmanned Aircraft System (UAS) operations has been observed over
the past decade, largely driven by the emergence of new commercial opportunities and use …

In this paper, the problem of path planning for active simultaneous localization and map**
(SLAM) is addressed. In order to improve its localization accuracy while autonomously …

Two-dimensional (2-D)/3-D pose-graph simultaneous localization and map** (SLAM) is a
problem of estimating a set of poses based on noisy measurements of relative rotations and …

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 …

In this paper we present a novel information-theoretic utility function for selecting actions in a
robot-based autonomous exploration task. The robot's goal in an autonomous exploration …

Robotic Information Gathering (RIG) is a foundational research topic that answers how a
robot (team) collects informative data to efficiently build an accurate model of an unknown …