# Fast Autonomous Robotic Exploration Using the Underlying Graph Structure

arXiv: https://arxiv.org/abs/2204.10610 · HTML: https://mlsanity.com/paper/2204.10610/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2022.

## Built on by (most cited first, 2 of 2)

- [SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems](https://mlsanity.com/paper/2306.12623/) (2023)
- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)

## Abstract (arXiv)

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.