LagMemo: 语言3D高斯点云记忆用于多模态开放词汇多目标视觉导航
LagMemo: Language 3D Gaussian Splatting Memory for Multi-modal Open-vocabulary Multi-goal Visual Navigation
AI总结：
LagMemo通过语言3D高斯点云记忆实现多模态开放词汇多目标视觉导航，有效提升多目标定位性能
摘要：
利用视觉信息导航到指定目标是智能机器人的一项基本能力。为应对多模态、开放词汇目标查询和多目标视觉导航的实践需求，我们提出了LagMemo，一种利用语言3D高斯点云记忆的导航系统。在一次探索过程中，LagMemo构建了一个具有强空间-语义关联的统一3D语言记忆。随着任务目标的输入，系统能够高效查询记忆，预测候选目标位置，并整合基于局部感知的验证机制以动态匹配和验证目标。为了公平和严谨的评估，我们从GOAT-Bench中提炼出高质量的核心分割数据集GOAT-Core。实验结果表明，LagMemo的记忆模块实现了有效的多模态开放词汇定位，并在多目标视觉导航中显著优于现有最先进方法。项目页面：https://weekgoodday.github.io/lagmemo
英文摘要：
Navigating to a designated goal using visual information is a fundamental capability for intelligent robots. To address the practical demands of multi-modal, open-vocabulary goal queries and multi-goal visual navigation, we propose LagMemo, a navigation system that leverages a language 3D Gaussian Splatting memory. During a one-time exploration, LagMemo constructs a unified 3D language memory with robust spatial-semantic correlations. With incoming task goals, the system efficiently queries the memory, predicts candidate goal locations, and integrates a local perception-based verification mechanism to dynamically match and validate goals. For fair and rigorous evaluation, we curate GOAT-Core, a high-quality core split distilled from GOAT-Bench. Experimental results show that LagMemo's memory module enables effective multi-modal open-vocabulary localization, and significantly outperforms state-of-the-art methods in multi-goal visual navigation. Project page: https://weekgoodday.github.io/lagmemo
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
