Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confronted with unfamiliar contexts, and it is challenging to obtain manual annotations frequently for new circumstances. In this paper, we introduce a method for learning traversability from images that utilizes only self-supervision and no manual labels, enabling it to easily learn traversability in new circumstances. To this end, we first generate self-supervised traversability labels from past driving trajectories by labeling regions traversed by the vehicle as highly traversable. Using the self-supervised labels, we then train a neural network that identifies terrains that are safe to traverse from an image using a one-class classification algorithm. Additionally, we supplement the limitations of self-supervised labels by incorporating methods of self-supervised learning of visual representations. To conduct a comprehensive evaluation, we collect data in a variety of driving environments and perceptual conditions and show that our method produces reliable estimations in various environments. In addition, the experimental results validate that our method outperforms other self-supervised traversability estimation methods and achieves comparable performances with supervised learning methods trained on manually labeled data.

翻译：估计地形的可通行性应在越野自动驾驶的不同环境下可靠且准确。然而，基于学习方法在面对陌生场景时往往产生不可靠的结果，且针对新环境频繁获取人工标注具有挑战性。本文提出一种仅利用自监督学习（无需人工标注）从图像中学习可通行性的方法，使其能够轻松适应新环境。为此，我们首先通过标记车辆已通过区域为高可通行性，从历史行驶轨迹生成自监督的可通行性标签。利用这些自监督标签，我们采用单类分类算法训练神经网络，从图像中识别安全通行的地形。此外，我们结合视觉表征自监督学习方法，弥补自监督标签的局限性。为进行全面评估，我们在多种驾驶环境和感知条件下采集数据，证明该方法在不同环境下均能产生可靠估计。实验结果进一步验证，本方法优于其他自监督可通行性估计方法，且性能与基于人工标注数据的监督学习方法相当。