SLIM-VDB：一种实时三维概率语义映射框架
SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
AI总结：
SLIM-VDB是一种基于OpenVDB数据结构的实时三维概率语义映射框架，实现了内存和整合时间的显著减少，同时保持了较高的映射精度。
摘要：
本文介绍了SLIM-VDB，一种新的轻量级语义映射系统，具有概率语义融合功能，适用于封闭集或开放集词典。计算机图形学社区的数据结构进步，如OpenVDB，已在体素场景表示中显著提高了计算和内存效率。尽管OpenVDB已被用于机器人应用中的几何映射，但使用OpenVDB进行场景理解的语义映射仍处于未探索状态。此外，现有的语义映射系统缺乏在单一框架内整合固定类别和开放语言标签预测的支持。在本文中，我们提出了一种新的三维语义映射系统，利用OpenVDB数据结构，并集成了统一的贝叶斯更新框架，用于封闭集和开放集语义融合。我们提出的方法SLIM-VDB在内存和整合时间上相比当前最先进的语义映射方法有显著减少，同时保持了可比的映射精度。一个开源的C++代码库配有Python接口，可在https://github.com/umfieldrobotics/slim-vdb上获得。
英文摘要：
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.
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。
