Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can accurately predict traversability in a variety of environments. This paper presents METAVerse, a meta-learning framework for learning a global model that accurately and reliably predicts terrain traversability across diverse environments. We train the traversability prediction network to generate a dense and continuous-valued cost map from a sparse LiDAR point cloud, leveraging vehicle-terrain interaction feedback in a self-supervised manner. Meta-learning is utilized to train a global model with driving data collected from multiple environments, effectively minimizing estimation uncertainty. During deployment, online adaptation is performed to rapidly adapt the network to the local environment by exploiting recent interaction experiences. To conduct a comprehensive evaluation, we collect driving data from various terrains and demonstrate that our method can obtain a global model that minimizes uncertainty. Moreover, by integrating our model with a model predictive controller, we demonstrate that the reduced uncertainty results in safe and stable navigation in unstructured and unknown terrains.

翻译：在越野条件下的自主导航需要对地形可通行性进行精确估计。然而，非结构化环境中的可通行性估计由于影响车辆-地形相互作用的众多因素的变异性而具有高度不确定性。因此，获得一个能在多种环境中准确预测可通行性的泛化模型极具挑战性。本文提出METAVerse，一种元学习框架，旨在学习一个全局模型，该模型能够跨不同环境准确且可靠地预测地形可通行性。我们训练可通行性预测网络，从稀疏激光雷达点云生成密集且连续的代价地图，并以自监督方式利用车辆-地形交互反馈。通过元学习，利用从多种环境收集的驾驶数据训练全局模型，有效最小化估计不确定性。在部署阶段，进行在线自适应，通过利用近期交互经验快速将网络适应至局部环境。为进行全面评估，我们收集了来自不同地形的驾驶数据，并证明我们的方法能够获得最小化不确定性的全局模型。此外，通过将模型与模型预测控制器集成，我们证明降低的不确定性实现了在非结构化与未知地形中的安全稳定导航。