SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
This work addresses the need for efficient and flexible semantic mapping in robotics, enabling real-time scene understanding with support for both fixed and open-language labels, though it is incremental in leveraging existing data structures for a new application.
The paper tackles the problem of 3D semantic mapping by introducing SLIM-VDB, a lightweight framework that uses OpenVDB for efficient volumetric representation and integrates a Bayesian update for both closed- and open-set semantic fusion, achieving significant reductions in memory and integration times compared to state-of-the-art approaches while maintaining comparable accuracy.
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.