Active SLAM Utility Function Exploiting Path Entropy

TL;DR

This paper introduces a utility function for Active SLAM that combines map entropy and D-Optimality metrics to improve frontier goal selection, leading to increased environment coverage.

Contribution

The paper presents a novel utility function for Active SLAM that exploits path entropy and D-Optimality to enhance exploration efficiency and map coverage.

Findings

Achieved 32% more coverage in simulations and experiments.

Utilized map entropy and D-Optimality for better frontier goal selection.

Validated improvements using publicly available datasets.

Abstract

In this article we present a utility function for Active SLAM (A-SLAM) which utilizes map entropy along with D-Optimality criterion metrices for weighting goal frontier candidates. We propose a utility function for frontier goal selection that exploits the occupancy grid map by utilizing the path entropy and favors unknown map locations for maximum area coverage while maintaining a low localization and mapping uncertainties. We quantify the efficiency of our method using various graph connectivity matrices and map efficiency indexes for an environment exploration task. Using simulation and experimental results against similar approaches we achieve an average of 32% more coverage using publicly available data sets.