Point Cloud Registration for Visual Geo-Referenced Localization Between Aerial and Ground Robots

Gonzalo Garcia, Azim Eskandarian

2025

Abstract

Cooperative perception between aerial and ground robots relies on the accurate alignment of spatial data collected from different platforms, often operating under diverse viewpoints and sensor constraints. In this work, point cloud registration techniques for monocular visual SLAM-generated maps are investigated, which are common in lightweight autonomous systems due to their low cost and sensor simplicity. However, monocular visual SLAM outputs are typically sparse and suffer from scale ambiguity, posing significant challenges for map fusion. We evaluate registration pipelines combining coarse global feature matching with local refinement methods, including point-to-plane and plane-to-plane Iterative Closest Point alignments, to address these issues. Our approach emphasizes robustness to differences in scale, density, and perspective. Additionally, we assess the consistency of the resulting estimated trajectories to support geo-referenced localization across platforms. Experimental results using datasets from both aerial and ground robots demonstrate that the proposed methods improve spatial coherence by a factor of over 4 based on statistical metrics, and enable collaborative mapping and localization in GNSS-intermittent environments. This work can contribute to advancing multi-robot coordination for real-world tasks such as infrastructure inspection, exploration, and disaster response.

Paper Citation

in Harvard Style

Garcia G. and Eskandarian A. (2025). Point Cloud Registration for Visual Geo-Referenced Localization Between Aerial and Ground Robots. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 211-218. DOI: 10.5220/0013693500003982

in Bibtex Style

@conference{icinco25,
author={Gonzalo Garcia and Azim Eskandarian},
title={Point Cloud Registration for Visual Geo-Referenced Localization Between Aerial and Ground Robots},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={211-218},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013693500003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Point Cloud Registration for Visual Geo-Referenced Localization Between Aerial and Ground Robots
SN - 978-989-758-770-2
AU - Garcia G.
AU - Eskandarian A.
PY - 2025
SP - 211
EP - 218
DO - 10.5220/0013693500003982
PB - SciTePress