Multi-Agent Monocular SLAM

Pieter Beerten, Charles Hamesse, Charles Hamesse, Rob Haelterman

2024

Abstract

This article describes the development of an optimization method for multi-agent monocular SLAM systems. These systems allow autonomous robots to create a map of an unknown environment and to simultaneously localize themselves within it. The proposed multi-agent system combines measurements made by independent agents to increase the accuracy of the estimated poses of the agents and the created map. Our method is based on the single-agent monocular ORB-SLAM2 framework, and we develop a complete multi-agent optimization post-processing algorithm, effectively refining all camera trajectories and map points. Our experiments on the EuRoC machine hall dataset show that we can successfully combine the information of multiple SLAM agents to increase the accuracy of the estimated trajectories.

Paper Citation

in Harvard Style

Beerten P., Hamesse C. and Haelterman R. (2024). Multi-Agent Monocular SLAM. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 213-220. DOI: 10.5220/0012320000003636

in Bibtex Style

@conference{icaart24,
author={Pieter Beerten and Charles Hamesse and Rob Haelterman},
title={Multi-Agent Monocular SLAM},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={213-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012320000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Multi-Agent Monocular SLAM
SN - 978-989-758-680-4
AU - Beerten P.
AU - Hamesse C.
AU - Haelterman R.
PY - 2024
SP - 213
EP - 220
DO - 10.5220/0012320000003636
PB - SciTePress