CollisionSplatting: 3DGSシーンにおける衝突認識型運動計画と画像条件付き目的関数および調整可能な保守性
CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism
3Dガウシアンスプラッティング(3DGS)シーン上で直接動作する、保守性を調整可能な確率的距離指標を提案し、画像条件付き報酬関数と組み合わせてGPU加速MPPIおよびRRTプランナに統合することで、視覚誘導ナビゲーションとマニピュレーションを実現した。
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著者: R. Khorrambakht, Joaquim Ortiz-Haro, Stephan Weiss, Ludovic Righetti
分類: cs.RO
原文アブストラクト
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.