ProTerrain：基于概率物理信息的崎岖地形世界建模
ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling
AI总结：
针对越野环境下现有地形不确定性预测方法忽略空间局部相关性的问题，提出ProTerrain概率框架，建模空间相关的地形偶然不确定性并通过可微物理引擎传播，实现高精度高分辨率的概率轨迹预测。
摘要：
在非结构化越野环境中，地形具有异质性且感知不确定性很高，具备不确定性感知能力的机器人运动预测对下游可通过性估计与安全自主导航至关重要。现有方法大多假设地形不确定性是确定性的或空间独立的，忽略了3D空间数据固有的局部相关性，往往产生不可靠的预测结果。本研究提出一种高效概率框架，将地形参数上空间相关的偶然不确定性显式建模为概率世界模型，并通过可微物理引擎传播该不确定性以实现概率轨迹预测。借助结构化卷积算子，本方法在可控计算成本下提供高分辨率多变量预测。在公开数据集上的实验评估表明，与偶然不确定性估计基线方法相比，本方法的不确定性估计与轨迹预测精度均有显著提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
