提出可泛化的主动建图策略，显著提升复杂室内环境探索效率
GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes
- 采用语义表征与长期可导航目标，实现跨场景泛化
- 在128个未见场景中达66.50%覆盖率，比现有方法高9.49%
- 适用于复杂布局的移动机器人自主探索任务
在复杂未知环境中实现可泛化的主动建图仍是移动机器人的关键挑战。现有方法受限于训练数据不足和保守探索策略，在不同布局与复杂连接场景间泛化能力有限。为此，我们引入GLEAM-Bench，首个面向可泛化主动建图的大规模基准，包含1,152个来自合成与真实扫描数据集的多样化3D场景。在此基础上，提出GLEAM，一种统一的可泛化探索策略。其优异泛化性主要源于语义表征、长期可导航目标及随机化策略。在128个未见复杂场景上，显著优于现有最先进方法，实现66.50%的覆盖率（+9.49%），并生成更高效轨迹与更高精度地图。项目页面：https://xiao-chen.tech/gleam/
原文摘要 · Abstract (English)
Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conservative exploration strategies, exhibit limited generalizability across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we introduce GLEAM-Bench, the first large-scale benchmark designed for generalizable active mapping with 1,152 diverse 3D scenes from synthetic and real-scan datasets. Building upon this foundation, we propose GLEAM, a unified generalizable exploration policy for active mapping. Its superior generalizability comes mainly from our semantic representations, long-term navigable goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes. Project page: https://xiao-chen.tech/gleam/.
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