LOST-3DSG: 轻量级开放词汇3D场景图在动态环境中的语义跟踪
LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments
AI总结：
LOST-3DSG通过语义跟踪方法实现轻量级开放词汇3D场景图，在动态环境中高效跟踪物体。
摘要：
在动态环境中跟踪移动物体是机器人领域的一个核心挑战。最近的研究在该领域取得了显著进展；然而，许多现有方法仍效率低下，因为它们依赖于重型基础模型。为了解决这一限制，我们提出了LOST-3DSG，一种轻量级开放词汇3D场景图，旨在在现实环境中跟踪动态物体。我们的方法采用基于word2vec和句子嵌入的语义方法进行实体跟踪，从而实现开放词汇表示，同时避免存储密集的CLIP视觉特征。结果表明，与依赖高维视觉嵌入的方法相比，LOST-3DSG实现了更优越的性能。我们通过在TIAGo机器人上使用真实3D环境进行定性和定量实验来评估我们的方法。结果证明了LOST-3DSG在动态物体跟踪中的有效性和效率。代码和补充材料可在项目网站上公开获取：https://lab-rococo-sapienza.github.io/lost-3dsg/。
英文摘要：
Tracking objects that move within dynamic environments is a core challenge in robotics. Recent research has advanced this topic significantly; however, many existing approaches remain inefficient due to their reliance on heavy foundation models. To address this limitation, we propose LOST-3DSG, a lightweight open-vocabulary 3D scene graph designed to track dynamic objects in real-world environments. Our method adopts a semantic approach to entity tracking based on word2vec and sentence embeddings, enabling an open-vocabulary representation while avoiding the necessity of storing dense CLIP visual features. As a result, LOST-3DSG achieves superior performance compared to approaches that rely on high-dimensional visual embeddings. We evaluate our method through qualitative and quantitative experiments conducted in a real 3D environment using a TIAGo robot. The results demonstrate the effectiveness and efficiency of LOST-3DSG in dynamic object tracking. Code and supplementary material are publicly available on the project website at https://lab-rococo-sapienza.github.io/lost-3dsg/.
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。
