Abstract
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 this https URL.
Abstract (translated)
在机器人技术中，追踪动态环境中移动的对象是一个核心挑战。最近的研究在这个领域取得了显著的进展；然而，许多现有的方法由于依赖于重型基础模型而仍然不够高效。为了解决这一限制，我们提出了一种轻量级的方法——LOST-3DSG（轻量级开放词汇3D场景图），旨在追踪真实世界环境中动态对象。 我们的方法采用基于word2vec和句子嵌入的语义方法进行实体追踪，这使得它能够实现开放词汇表表示同时避免了存储密集型CLIP视觉特征的需求。结果表明，与依赖于高维视觉嵌入的方法相比，LOST-3DSG在性能上更胜一筹。 我们通过使用TIAGo机器人在真实3D环境中进行的定性和定量实验来评估我们的方法。实验结果证明了LOST-3DSG在动态对象追踪中的有效性和高效性。该项目的相关代码和补充材料可以在项目网站（此处链接为原文中提到的具体URL，您可以直接访问该网址获取详细信息）上公开获得。 原文翻译： 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 this https URL.
URL
https://arxiv.org/abs/2601.02905
