用OpenVDB实现轻量实时3D语义地图，支持固定与开放标签融合。
SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
- 基于OpenVDB结构实现高效3D语义建图
- 内存和融合时间显著降低，精度相当
- 适合需要实时语义理解的机器人应用
本文提出SLIM-VDB，一种轻量级的3D概率语义映射框架，支持封闭集与开放集词典的语义融合。计算机图形学领域的数据结构（如OpenVDB）在体素场景表示中已展现出显著的计算与内存效率优势。尽管OpenVDB已被用于机器人几何建图，但其在语义建图中的应用仍属空白。现有语义建图系统无法在同一框架内整合固定类别与开放语言标签预测。为此，我们提出一种新方法，利用OpenVDB结构并集成统一的贝叶斯更新机制，实现闭集与开集语义融合。SLIM-VDB相比当前最优方法，在内存占用与融合时间上均有显著降低，同时保持相近的建图精度。开源C++代码库（含Python接口）已发布于https://github.com/umfieldrobotics/slim-vdb。
原文摘要 · Abstract (English)
This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures from the computer graphics community, such as OpenVDB, have demonstrated significantly improved computational and memory efficiency in volumetric scene representation. Although OpenVDB has been used for geometric mapping in robotics applications, semantic mapping for scene understanding with OpenVDB remains unexplored. In addition, existing semantic mapping systems lack support for integrating both fixed-category and open-language label predictions within a single framework. In this paper, we propose a novel 3D semantic mapping system that leverages the OpenVDB data structure and integrates a unified Bayesian update framework for both closed- and open-set semantic fusion. Our proposed framework, SLIM-VDB, achieves significant reduction in both memory and integration times compared to current state-of-the-art semantic mapping approaches, while maintaining comparable mapping accuracy. An open-source C++ codebase with a Python interface is available at https://github.com/umfieldrobotics/slim-vdb.
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