论文标题
移动机器人的遍历性估计调查
A Survey of Traversability Estimation for Mobile Robots
论文作者
论文摘要
遍历性说明了驾驶特定区域的难度，并包括基于其物理特性（例如斜率和粗糙度，表面状况等）的地形对于遍历的适用性。在这项调查中，我们强调了遍历性估算技术演变的所有主要步骤的优点和局限性，从而涵盖了不可培养和机器的方法，并涵盖了最近的文献。我们讨论深度学习的努力如何为遍历性估计的根本改善创造了机会。最后，我们讨论了自我监督的学习如何帮助满足深度方法对大规模数据集的增加（挑战和标签）的需求。
Traversability illustrates the difficulty of driving through a specific region and encompasses the suitability of the terrain for traverse based on its physical properties, such as slope and roughness, surface condition, etc. In this survey we highlight the merits and limitations of all the major steps in the evolution of traversability estimation techniques, covering both non-trainable and machine-learning methods, leading up to the recent proliferation of deep learning literature. We discuss how the nascence of Deep Learning has created an opportunity for radical improvement in traversability estimation. Finally, we discuss how self-supervised learning can help satisfy deep methods' increased need for (challenging to acquire and label) large-scale datasets.
