Discriminating the traversability of terrains is a crucial task for autonomous driving in off-road environments. However, it is challenging due to the diverse, ambiguous, and platform-specific nature of off-road traversability. In this paper, we propose a novel self-supervised terrain traversability learning framework, utilizing a contrastive label disambiguation mechanism. Firstly, weakly labeled training samples with pseudo labels are automatically generated by projecting actual driving experiences onto the terrain models constructed in real time. Subsequently, a prototype-based contrastive representation learning method is designed to learn distinguishable embeddings, facilitating the self-supervised updating of those pseudo labels. As the iterative interaction between representation learning and pseudo label updating, the ambiguities in those pseudo labels are gradually eliminated, enabling the learning of platform-specific and task-specific traversability without any human-provided annotations. Experimental results on the RELLIS-3D dataset and our Gobi Desert driving dataset demonstrate the effectiveness of the proposed method.

翻译：地形可通行性判别是越野环境下自动驾驶的关键任务，然而由于越野可通行性具有多样性、模糊性和平台特异性，该任务极具挑战性。本文提出一种新颖的自监督地形可通行性学习框架，采用对比标签消歧机制。首先，通过将实际驾驶经验实时投影到构建的地形模型上，自动生成带有伪标签的弱标注训练样本。随后设计基于原型的对比表征学习方法，学习可区分的嵌入特征，促进伪标签的自监督更新。通过表征学习与伪标签更新的迭代交互，伪标签中的模糊性逐步消除，无需任何人工标注即可学习平台特异性和任务特异性的可通行性。在RELLIS-3D数据集及我们构建的戈壁沙漠驾驶数据集上的实验结果表明了该方法的有效性。