SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework

Anja Sheppard; Parker Ewen; Joey Wilson; Advaith V. Sethuraman; Benard Adewole; Anran Li; Yuzhen Chen; Ram Vasudevan; Katherine A. Skinner

Paper

SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework

Abstract

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.