EVSMap: An Efficient Volumetric-Semantic Mapping Approach for Embedded Systems

Despite significant progress in perception tasks such as 3D scene mapping and semantic information extraction using SLAM and deep learning, applying these techniques within computationally constrained embedded systems remains a challenge. In this work, we introduce a novel end-to-end framework for efficient and real-time volumetric-semantic mapping. We have developed a lightweight and robust RGB-D segmentation network for extracting semantic information. Through the introduction of three distinct modules—CFIM, DAPPF, and LAD—our network significantly enhances real-time performance while achieving Mean Intersection over Union (MIoU) scores comparable to state-of-the-art (SOTA) models. Our model reduces the parameters by 8 to 26 times compared to similar networks and improves inference speed by 2 to 3 times. Additionally, we improved a multi-class bayesian updating strategy by refining penalty function to reduce the memory size of the semantic map and enhance the mapping speed. Compared with other volumetric-semantic mapping approaches, our work maintains the same level of detail in semantic information representation, while increasing mapping speed by 1.3 to 9.6 times and reducing memory size of the map by up to 2.6 times. Finally, we applied our work to real-world mobile robot exploration scenarios, demonstrating the efficiency of the proposed framework.