OGS-SLAM: Hybrid ORB-Gaussian Splatting SLAM

Traditional visual SLAM systems (dense and sparse) focus on building metric maps, but the internal representations are misaligned with human vision, making it insufficient for assisting robots in scene perception and interpretation. Conversely, aligning robot scene representation with human vision enables more intuitive human-to-robot commands and improves the generalization capability of deployed neural networks trained on natural images. Neural scene representation-based visual SLAM system, with its consistent and high-fidelity mapping, provides a novel way to assist robots in detailed scene depiction and comprehensive perception. However, end-to-end methods suffer from low accuracy in robot localization, which inevitably degrades mapping quality and limits their practical applications. In this paper, we propose a robust hybrid SLAM system, named OGS-SLAM, which integrates traditional visual SLAM with 3D Gaussian Splatting (3D GS) mapping. This system inherits the high localization accuracy of traditional SLAM while providing a scene model that aligns with human cognition, thereby offering a reliable foundation for downstream human-robot interaction tasks. Experiments demonstrate that our method outperforms state-of-the-art (SOTA) end-to-end SLAM systems in localization, mapping, and map semantic segmentation. Code will be available at: https://github.com/realXiaohan/OGS-SLAM.