多机器人协同3D重建，用语义+不确定性优化视角规划
Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance
- 融合语义分割与3D高斯的表面不确定性，动态聚焦关键区域
- 多机器人协同规划效率高于现有方法，重建质量最优
- 适用于大场景快速重建，适合真实多机协同应用
隐式神经表示和3D高斯点阵（3DGS）在场景重建中展现出巨大潜力。近期研究通过任务分配方法将其拓展至自主重建，但主要局限于单机器人，且大规模场景的快速重建仍具挑战。此外，基于表面不确定性的任务驱动规划易陷入局部最优。为此，我们提出首个基于3DGS的集中式多机器人自主3D重建框架。为降低任务生成耗时并提升重建质量，我们引入在线开放词汇语义分割与3DGS表面不确定性，聚焦于实例不确定性高的区域进行视点采样。最后，设计了包含模式与任务分配的多机器人协作策略，在保证规划效率的同时提升重建质量。实验表明，本方法在所有规划方法中重建质量最高，且规划效率优于现有多机器人方法。在多机器人部署中，结果证明其能有效规划视点路径并实现高质量场景重建。
原文摘要 · Abstract (English)
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 single robot, and rapid reconstruction of large-scale scenes remains challenging. Additionally, 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 time cost of task generation and improve reconstruction quality, we integrate online open-vocabulary semantic segmentation 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 results show that it can effectively plan view paths and reconstruct scenes with high quality.
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