用视觉线索提升机器人探索效率，比传统方法快15%。
FrontierNet: Learning Visual Cues to Explore
- 仅用带单目深度先验的RGB图像预测探索目标和信息增益。
- 在模拟与真实环境中实现早期探索效率提升15%。
- 适合需要低依赖地图、高视觉感知的自主机器人场景。
自主机器人探索未知环境至关重要，使其能主动推理并决定获取哪些新数据以完成建图、物体发现和环境评估等任务。现有方案如基于前缘的探索方法严重依赖3D地图运算，受限于地图质量，且常忽略视觉线索中的重要上下文信息。本文提出一种纯视觉的前缘探索系统，核心为FrontierNet模型，该模型从带有单目深度先验的带位姿RGB图像中（i）生成前缘候选点，（ii）预测其信息增益。相比传统依赖3D地图的目标提取方法，本方法在大量仿真与真实实验中实现了早期探索效率15%的提升。项目代码已开源：https://github.com/cvg/FrontierNet。
原文摘要 · Abstract (English)
Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for different tasks, such as mapping, object discovery, and environmental assessment. Existing solutions, such as frontier-based exploration approaches, rely heavily on 3D map operations, which are limited by map quality and, more critically, 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 visual-only frontier-based exploration system, with FrontierNet as its core component. FrontierNet is a learning-based model that (i) proposes 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 goal-extraction approaches, achieving a 15\% improvement in early-stage exploration efficiency, as validated through extensive simulations and real-world experiments. The project is available at https://github.com/cvg/FrontierNet.
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