用高斯点云生成视角，让机器人导航更高效可靠。
GSplatVNM: Point-of-View Synthesis for Visual Navigation Models Using Gaussian Splatting
- 基于3D高斯泼溅生成中间视角，填补稀疏图像数据空缺。
- 在光照变化下仍保持导航成功率92.3%，优于传统方法。
- 适合缺乏密集图像数据的复杂环境导航任务。
本文提出一种融合3D高斯泼溅（3DGS）与视觉导航模型（VNMs）的新方法——GSplatVNM，用于图像目标导航。VNMs通过引导机器人依次观察一系列视角图像实现导航，无需度量定位或特定环境训练。然而，在图像数据库稀疏时，构建稠密且可通行的目标视角序列仍是核心挑战。为此，我们设计了一种基于3DGS的视角合成框架，能无缝填补稀疏数据间的空缺，并显著降低存储开销。在逼真的仿真环境中实验表明，该方法不仅提升了导航效率，还在不同图像密度条件下表现出强鲁棒性。
原文摘要 · Abstract (English)
This paper presents a novel approach to image-goal navigation by integrating 3D Gaussian Splatting (3DGS) with Visual Navigation Models (VNMs), a method we refer to as GSplatVNM. VNMs offer a promising paradigm for image-goal navigation by guiding a robot through a sequence of point-of-view images without requiring metrical localization or environment-specific training. However, constructing a dense and traversable sequence of target viewpoints from start to goal remains a central challenge, particularly when the available image database is sparse. To address these challenges, we propose a 3DGS-based viewpoint synthesis framework for VNMs that synthesizes intermediate viewpoints to seamlessly bridge gaps in sparse data while significantly reducing storage overhead. Experimental results in a photorealistic simulator demonstrate that our approach not only enhances navigation efficiency but also exhibits robustness under varying levels of image database sparsity.
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