外观
论文arXiv 2610.1175220 天前
2DGS-Planner：基于栅格化的2D Gaussian Splatting地图路径规划
原文链接：2DGS-Planner: Rasterization-based Path Planning in 2D Gaussian Splatting Map（arXiv 2610.11752 · 发布日期：2026-10-08）
2DGS-Planner: Rasterization-based Path Planning in 2D Gaussian Splatting Map
提出2DGS-Planner，通过栅格化从2DGS地图读取几何信息，在地面机器人路径规划实验中优于测试基线。
核心要点
- 单个Gaussian基元由有限视角重建联合优化，未必可靠表示障碍物。
- 离线构建路网时，多视角归因生成结构分数，引导地面自适应节点采样。
- 候选边由路径对齐正交查询验证，圆柱查询估计并缓存局部间隙场。
- 实验显示连通性、间隙估计和规划成功率优于测试基线；代码和数据已公开。
验证范围与局限：验证范围为2DGS地图上的地面机器人路径规划，摘要未说明仿真或真机设置。
英文摘要 (Abstract)
Gaussian splatting provides an explicit and efficiently rasterizable scene representation for robot navigation. However, individual Gaussian primitives may not reliably represent obstacles as they are jointly optimized through alpha-composited rendering from a finite set of reconstruction views. We propose 2DGS-Planner, a path planner for ground robots that reads planning-relevant geometry from a 2D Gaussian splatting (2DGS) map through rasterization, rather than treating individual Gaussian primitives as obstacles. During offline roadmap construction, multi-view attribution converts rendered normal dispersion into structural scores for non-ground disks supported by the reconstruction views. These scores guide adaptive node sampling on the ground. Path-aligned orthographic queries validate candidate edges, while cylindrical queries estimate local clearance fields that are cached on the edges. During online planning, graph search initializes a route, and path refinement reuses the cached fields while accounting for the robot's dimensions and ground constraints. Experiments demonstrate improved roadmap connectivity, more accurate clearance estimation, and higher planning success compared with the tested baselines. These results support rasterization as an effective geometric query interface for planning directly on Gaussian maps. Code and data are available at https://2dgs-planner.github.io/
作者：Jiwon Park, Dong-Uk Seo, Hyun Myung
来源
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