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 is available at https://github.com/robin-karlsson0/dslp.

翻译：人类通过遵守共同已知的导航模式（可表示为定向路径或道路车道）来协作导航于规则约束环境中。从不完全观测环境中推断这些导航模式，是智能移动机器人在未测绘区域运行的关键需求。然而，以算法定义这些导航模式并非易事。本文提出首个自监督学习方法，仅通过部分观测学习推断真实环境中的导航模式。我们阐述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器，使模型在无限数据极限下能够预测无偏的局部定向软车道概率场。我们展示了如何通过将最大似然图拟合到定向软车道概率场来推断全局导航模式。实验表明，在nuScenes数据集上，我们的自监督学习模型优于两种当前最优的有监督车道图预测模型。我们提出的自监督学习方法可作为一种可扩展且可解释的持续学习范式用于感知导航。代码见https://github.com/robin-karlsson0/dslp。