建模地形不确定性，让机器人更安全穿越复杂野外环境。
ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling
- 用概率世界模型捕捉地形参数的空间相关性不确定性
- 在公开数据集上显著提升轨迹预测准确率和不确定性估计效果
- 适合做无人车、机器人在非结构化地形的导航系统研究
在非结构化、非道路环境中，不确定性的感知对下游可通行性评估和安全自主导航至关重要，因地形异质性强且感知不确定性高。现有方法多假设确定性或空间独立的地形不确定性，忽略了三维空间数据的固有局部相关性，常导致不可靠预测。本文提出一种高效的概率框架，将地形参数的时空相关随机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。利用结构化卷积算子，方法在可接受计算成本下实现高分辨率多变量预测。在公开数据集上的实验表明，相比基准方法，本方法在不确定性估计和轨迹预测精度上均有显著提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。