外观
论文arXiv 2609.356191 个月前
CollisionSplatting：3DGS场景中具有图像条件目标与可调保守性的碰撞感知运动规划
原文链接：CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism（arXiv 2609.35619 · 发布日期：2026-09-28）
CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism
CollisionSplatting在3DGS场景上用可调距离度量结合图像条件奖励，实现碰撞感知视觉规划并在真机导航与操作中验证。
核心要点
- 密集视觉信息难融入运动规划：几何表示丢失视觉细节，学习视觉模型常缺几何可解释性与效率。
- 提出在标准3DGS上运行的GPU加速、概率启发距离度量，并可调节规划保守性。
- 结合图像条件奖励，统一碰撞感知代价与图像空间目标，并接入MPPI和RRT规划器。
- 实验显示碰撞分类相当或更好、检测吞吐更高且VRAM更低，并用于真机视觉导航和操作。
验证范围与局限：验证范围包括碰撞分类对比，以及真实世界视觉引导导航和操作任务。
英文摘要 (Abstract)
Incorporating dense visual information into motion planning remains challenging, as geometric planners rely on abstracted scene representations that discard visual richness, while learned visual models often lack geometric interpretability and computational efficiency. This paper introduces CollisionSplatting, a simple, modular, GPU-accelerated, probability-inspired distance metric with tunable conservatism that operates directly on standard 3D Gaussian Splatting (3DGS) scenes. When combined with learned image-conditioned reward functions, this metric enables joint geometric and visual planning by unifying collision-aware costs with image-space objectives. We integrate the metric into GPU-accelerated Model Predictive Path Integral (MPPI) and Rapidly-Exploring Random Tree (RRT) planners, and show on-par or better collision-classification performance compared to representative baselines while achieving substantially higher collision-checking throughput and significantly lower VRAM usage. Finally, we demonstrate the effectiveness of our metric in real-world vision-guided navigation and manipulation tasks, highlighting 3DGS as a practical bridge between rich perception and real-time motion planning.
作者：Rooholla Khorrambakht, Joaquim Ortiz-Haro, Stephan Weiß, Ludovic Righetti
来源
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