# GS-PRM：用于图像目标导航的 Gaussian 视觉路线图

> 原文链接：https://robotics.hellobike.com/papers/2026-10-14-2610-19541
> 频道：papers | 更新日期：2026-10-14 | 标签：arxiv, cs.RO
> 核心摘要：针对图像目标导航的全局规划需求，GS-PRM用3DGS渲染构建视觉路线图，并在仿真和真实环境中验证。

# GS-PRM：用于图像目标导航的 Gaussian 视觉路线图

::: v-pre
> 原文链接：[GS-PRM: Gaussian Visual Roadmap for Image-Goal Navigation](https://arxiv.org/abs/2610.19541)（arXiv 2610.19541 · 发布日期：2026-10-14）

**GS-PRM: Gaussian Visual Roadmap for Image-Goal Navigation**

针对图像目标导航的全局规划需求，GS-PRM用3DGS渲染构建视觉路线图，并在仿真和真实环境中验证。

## 核心要点

- 图像目标导航需要连接视觉端点的全局规划器，并提供局部策略可跟随的观测。
- GS-PRM直接通过3DGS渲染构建里程碑、局部连接和视觉参考，不生成占据栅格或SDF。
- 方法定义基于渲染的无碰撞配置与路径，用全景深度和正交渲染检验净空与扫掠体积。
- 仿真和真实实验显示，相比一种3DGS导航系统，平均成功率最高两倍，完成时行程时间最多降57.2%。

**验证范围与局限**：验证范围为仿真与真实环境中的长时程导航，使用目标条件策略；摘要未说明机器人数量。

## 英文摘要 (Abstract)

Image-goal navigation requires a global planner that connects visually specified endpoints while providing observations that a local policy can follow. Three-dimensional Gaussian Splatting (3DGS) provides map-registered RGB, opacity, and depth renderings, but does not explicitly encode navigable topology. We introduce GS-PRM, a Gaussian visual roadmap whose milestones, local connections, and visual references are constructed directly through 3DGS rendering without deriving an occupancy grid or signed distance field. We define render-based collision-free (RC-free) configurations and paths. Panoramic depth supplies conservative clearance for milestone validation and adaptive coverage, while edge-aligned orthographic rendering tests the robot's swept volume. We establish conditional soundness and sufficient conditions for deterministic query completeness and probabilistic construction completeness of GS-PRM. To validate the performance of GS-PRM, we conduct experiments in both simulated and real-world environments, demonstrating long-horizon navigation using goal-conditioned policies guided by GS-PRM. Compared with a state-of-the-art 3DGS-based navigation system, our GS-PRM-based navigation system achieves up to a twice higher average success rate and reduces travel time by up to 57.2% when both systems complete the navigation task.

作者：Ashraf Siddique, Kyung Min Han, Young J. Kim
:::

## 原始参考来源 (Sources)

- [arXiv 2610.19541：GS-PRM: Gaussian Visual Roadmap for Image-Goal Navigation](https://arxiv.org/abs/2610.19541)