Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enables our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception. Code released upon publication.

翻译：人类通过遵循彼此已知的导航模式（可表示为方向性路径或车道）在规则约束环境中进行协作导航。对于在未测绘区域运行的智能移动机器人而言，从非完整观测环境中推断这些导航模式至关重要。然而，通过算法定义这些导航模式并非易事。本文提出首个仅从部分观测中学习推断真实环境导航模式的自监督学习方法。我们阐释了如何通过几何数据增强、预测性世界建模及信息论正则化项，使模型在数据量趋于无穷时能够预测无偏的局部方向性软车道概率场。进一步展示如何通过在该场中拟合最大似然图来推断全局导航模式。实验表明，在nuScenes数据集上，我们的自监督模型优于两个最先进的监督式车道图预测模型。我们提出将该自监督方法作为面向感知导航的可扩展且可解释的持续学习范式。代码将于论文发表时开源。