Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for tasks such as mapping, object discovery, and environmental assessment. Existing methods, such as frontier-based methods, rely heavily on 3D map operations, which are limited by map quality and often overlook valuable context from visual cues. This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map. We propose a image-only frontier-based exploration system, with FrontierNet as a core component developed in this work. FrontierNet is a learning-based model that (i) detects frontiers, and (ii) predicts their information gain, from posed RGB images enhanced by monocular depth priors. Our approach provides an alternative to existing 3D-dependent exploration systems, achieving a 16% improvement in early-stage exploration efficiency, as validated through extensive simulations and real-world experiments.

本研究解决了自主机器人探索未知环境中依赖3D地图质量的局限性，提出使用2D视觉线索进行高效探索的新方法。核心组件FrontierNet是一个学习模型，能够从RGB图像中检测边界并预测信息增益。此方法在早期探索效率上提升了16%，为现有以3D为依赖的探索系统提供了替代方案。

边界网络：学习视觉线索以进行探索