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:对于在非结构化环境中自控车辆的安全和成功航行而言,地形的可穿越性应当根据车辆驾驶能力而有所不同。实际驾驶经验可以自我监督的方式用于学习车辆特有的可穿越性。然而,现有的自控可穿越性学习方法对于学习各种车辆的可穿越性来说并不是高度可伸缩的。在这项工作中,我们引入了一个可扩缩的框架,用于学习自控的可穿越性,这种框架可以直接从无人监督的车辆-地形互动中学习可穿越性。我们训练了一个神经网络,预测车辆从3D点云中将经历的自行感知性经验。使用一种新型的PU学习方法,网络同时查明了无法自行监督的可穿越性区域,因为从模拟和现实世界中收集的各种车辆的驾驶数据,我们显示我们的框架能够学习各种车辆的可自行监督的可穿越性。通过将我们的框架与模型预测控制器结合起来,我们又展示了在有效导航中的估计可移动性结果,从而能够避免机动性车辆的机动性能力,从而根据机动性车辆的实验性特性,确定无法验证。