ProTerrain：確率的物理情報に基づく不整地ワールドモデリング
ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling
不整地走行のための確率的ワールドモデルを提案し、地形パラメータの空間相関を考慮した不確かさを微分可能物理エンジンで伝播させて軌道予測精度を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Golnaz Raja, Ruslan Agishev, Miloš Prágr, Joni Pajarinen, Karel Zimmermann, Arun Kumar Singh, Reza Ghabcheloo
分類: cs.RO
原文アブストラクト
Uncertainty-aware robot motion prediction is crucial for downstream traversability estimation and safe autonomous navigation in unstructured, off-road environments, where terrain is heterogeneous and perceptual uncertainty is high. Most existing methods assume deterministic or spatially independent terrain uncertainties, ignoring the inherent local correlations of 3D spatial data and often producing unreliable predictions. In this work, we introduce an efficient probabilistic framework that explicitly models spatially correlated aleatoric uncertainty over terrain parameters as a probabilistic world model and propagates this uncertainty through a differentiable physics engine for probabilistic trajectory forecasting. By leveraging structured convolutional operators, our approach provides high-resolution multivariate predictions at manageable computational cost. Experimental evaluation on a publicly available dataset shows significantly improved uncertainty estimation and trajectory prediction accuracy over aleatoric uncertainty estimation baselines.
