Fast Autonomous Robotic Exploration Using the Underlying Graph Structure

TL;DR

This paper establishes a theoretical link between optimality criteria and pose-graph connectivity in Active SLAM, demonstrating computational efficiency and introducing a novel framework that improves autonomous exploration.

Contribution

It defines the relationship between graph theory and optimal experimental design in SLAM and proposes a new Active SLAM method leveraging graph structure for better exploration.

Findings

Significant reduction in computational load using graph structure.

The proposed method outperforms traditional Active SLAM approaches.

Validated on 2D and 3D datasets with improved exploration efficiency.

Abstract

In this work, we fully define the existing relationships between traditional optimality criteria and the connectivity of the underlying pose-graph in Active SLAM, characterizing, therefore, the connection between Graph Theory and the Theory Optimal Experimental Design. We validate the proposed relationships in 2D and 3D graph SLAM datasets, showing a remarkable relaxation of the computational load when using the graph structure. Furthermore, we present a novel Active SLAM framework which outperforms traditional methods by successfully leveraging the graphical facet of the problem so as to autonomously explore an unknown environment.