Multi-Robot Autonomous 3D Reconstruction Using Gaussian Splatting With Semantic Guidance
Jing Zeng, Qi Ye, Tianle Liu, Yang Xu, Jin Li, Jinming Xu, Liang Li, Jiming Chen
Abstract
Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous reconstruction through task assignment methods. However, these methods are mainly limited to a single robot, and rapid reconstruction of large-scale scenes remains a challenge. In addition, task-driven planning based on surface uncertainty is prone to being trapped in local optima. To this end, we propose the first 3DGS-based centralized multi-robot autonomous 3D reconstruction framework. To further reduce the time cost of task generation and improve reconstruction quality, we integrate open-vocabulary semantic segmentation online with surface uncertainty of 3DGS, focusing view sampling on regions with high instance uncertainty. Finally, we develop a multi-robot collaboration strategy with mode and task assignments improving reconstruction quality while ensuring planning efficiency. Our method demonstrates the highest reconstruction quality among all planning methods and superior planning efficiency compared to existing multi-robot methods. We deploy our method on multiple robots and the results show that it can effectively plan view paths and reconstruct scenes with high quality.
BibTeX
@inproceedings{ral2025_multirobotautono,
title = {Multi-Robot Autonomous 3D Reconstruction Using Gaussian Splatting With Semantic Guidance},
author = {Jing Zeng and Qi Ye and Tianle Liu and Yang Xu and Jin Li and Jinming Xu and Liang Li and Jiming Chen},
booktitle = {RA-L 2025},
year = {2025}
}