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.

本文介绍了第一种基于自我监督学习方法来学习从局部观测中推断全局导航模式的方法，并通过使用几何数据增强、预测世界建模和信息理论正则化器等技术，可以预测局部方向软通道概率场，并且可以通过拟合局部方向软通道概率场中的最大似然图来推断全局导航模式，并且实验证明该方法优于现有的两种监督式车道图预测模型。

从部分观测中学习预测导航模式