For the safe and successful navigation of autonomous vehicles in unstructured environments, the traversability of terrain should vary based on the driving capabilities of the vehicles. Actual driving experience can be utilized in a self-supervised fashion to learn vehicle-specific traversability. However, existing methods for learning self-supervised traversability are not highly scalable for learning the traversability of various vehicles. In this work, we introduce a scalable framework for learning self-supervised traversability, which can learn the traversability directly from vehicle-terrain interaction without any human supervision. We train a neural network that predicts the proprioceptive experience that a vehicle would undergo from 3D point clouds. Using a novel PU learning method, the network simultaneously identifies non-traversable regions where estimations can be overconfident. With driving data of various vehicles gathered from simulation and the real world, we show that our framework is capable of learning the self-supervised traversability of various vehicles. By integrating our framework with a model predictive controller, we demonstrate that estimated traversability results in effective navigation that enables distinct maneuvers based on the driving characteristics of the vehicles. In addition, experimental results validate the ability of our method to identify and avoid non-traversable regions.

翻译：在非结构化环境中实现自动驾驶车辆安全且成功的导航，地形的可通行性应基于车辆的驾驶能力动态变化。实际驾驶经验可通过自监督方式用于学习特定车辆的可通行性。然而，现有自监督可通行性学习方法在扩展至不同车辆的可通行性学习时存在局限性。本文提出一种可扩展的自监督可通行性学习框架，该框架无需人工监督即可直接从车辆-地形交互中学习可通行性。我们训练一个神经网络，通过三维点云预测车辆将经历的体感经验。采用新型正无标注学习方法，该网络能同步识别估计可能过度自信的不可通行区域。通过从仿真和真实环境采集的多类型车辆驾驶数据，我们证明该框架能够有效学习不同车辆的自监督可通行性。将所提框架与模型预测控制器集成后，实验表明基于可通行性估计的导航能根据车辆行驶特性实现差异化机动行为。此外，实验结果验证了该方法识别并规避不可通行区域的有效性。