将高斯点阵与标定标记结合，提升机器人仿真中的定位精度
Fiducial Marker Splatting for High-Fidelity Robotics Simulations
- 用高斯点阵生成带标定标记的逼真场景
- 在温室场景中定位精度显著优于传统方法
- 适合需要精准定位的农业机器人仿真
高保真3D仿真对移动机器人训练至关重要，但传统基于网格的表示在复杂环境（如密集种植的温室）中常因遮挡和重复结构而失效。最近的神经渲染方法（如高斯点阵，GS）虽能实现出色视觉真实感，却难以集成机器人定位所需的标定标记。本文提出一种混合框架，融合GS的视觉真实感与结构化标记表示。核心贡献是高效生成GS基础上的标定标记（如AprilTags）的新算法，适用于杂乱场景。实验表明，该方法在效率和姿态估计精度上均优于传统图像拟合技术。进一步在温室仿真中验证了框架潜力——该场景因密集植被、相似元素和遮挡，极大考验感知能力，凸显本方法在实际应用中的价值。
原文摘要 · Abstract (English)
High-fidelity 3D simulation is critical for training mobile robots, but its traditional reliance on mesh-based representations often struggle in complex environments, such as densely packed greenhouses featuring occlusions and repetitive structures. Recent neural rendering methods, like Gaussian Splatting (GS), achieve remarkable visual realism but lack flexibility to incorporate fiducial markers, which are essential for robotic localization and control. We propose a hybrid framework that combines the photorealism of GS with structured marker representations. Our core contribution is a novel algorithm for efficiently generating GS-based fiducial markers (e.g., AprilTags) within cluttered scenes. Experiments show that our approach outperforms traditional image-fitting techniques in both efficiency and pose-estimation accuracy. We further demonstrate the framework's potential in a greenhouse simulation. This agricultural setting serves as a challenging testbed, as its combination of dense foliage, similar-looking elements, and occlusions pushes the limits of perception, thereby highlighting the framework's value for real-world applications.
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