用全局图推理与扩散模型结合，让机器人更高效地探索未知室内环境。
GUIDE: A Diffusion-Based Autonomous Robot Exploration Framework Using Global Graph Inference
- 构建区域评估的全局图，融合已知与预测空间信息
- 扩散策略减少去噪步数，生成更稳定的探索动作序列
- 实测覆盖速度提升18.3%，冗余移动减少34.9%
在结构化复杂室内环境中实现自主探索仍具挑战，现有方法常难以准确建模未观测区域并规划全局高效路径。为此，我们提出GUIDE——一种融合全局图推理与基于扩散决策的新探索框架。引入区域评估的全局图表示，整合已观测环境数据与未探索区域预测，并通过区域级评估机制优先可信结构推断，弱化不确定性预测。在此增强表征基础上，扩散策略网络生成稳定且具备前瞻性的动作序列，显著减少去噪步骤。大量仿真与真实场景部署表明，GUIDE持续优于当前最优方法，覆盖完成速度最快提升18.3%，冗余移动减少34.9%。
原文摘要 · Abstract (English)
Autonomous exploration in structured and complex indoor environments remains a challenging task, as existing methods often struggle to appropriately model unobserved space and plan globally efficient paths. To address these limitations, we propose GUIDE, a novel exploration framework that synergistically combines global graph inference with diffusion-based decision-making. We introduce a region-evaluation global graph representation that integrates both observed environmental data and predictions of unexplored areas, enhanced by a region-level evaluation mechanism to prioritize reliable structural inferences while discounting uncertain predictions. Building upon this enriched representation, a diffusion policy network generates stable, foresighted action sequences with significantly reduced denoising steps. Extensive simulations and real-world deployments demonstrate that GUIDE consistently outperforms state-of-the-art methods, achieving up to 18.3% faster coverage completion and a 34.9% reduction in redundant movements.
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