轻量级3D场景图实现动态环境物体语义追踪
LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments
- 用词向量与句嵌入实现开放词汇语义跟踪
- 相比高维视觉特征，内存占用降低60%以上
- 适合资源受限机器人实时追踪应用
在动态环境中追踪移动物体是机器人领域的核心挑战。现有方法虽有进展，但多依赖大型基础模型导致效率低下。为此，我们提出 LOST-3DSG，一种轻量级开放词汇3D场景图，用于真实环境中的动态物体追踪。该方法基于 word2vec 与句子嵌入的语义追踪机制，实现开放词汇表征，无需存储密集的 CLIP 视觉特征。实验在 TIAGo 机器人的真实3D环境中进行，定量与定性结果均表明，LOST-3DSG 在追踪性能与效率上优于依赖高维视觉嵌入的方法。代码与补充材料已公开于 https://lab-rococo-sapienza.github.io/lost-3dsg/。
原文摘要 · Abstract (English)
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/.
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