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RGB-D Perception-Enhanced 3D Gaussian Splatting SLAM: A Robust Framework for Mapping Underground Spaces.

Tao Yan, Xiaohu Lin, Wanqiang Yao

    IEEE Transactions on Visualization and Computer Graphics
    |April 22, 2026
    PubMed
    Summary

    This study introduces an enhanced 3D Gaussian Splatting Simultaneous Localization and Mapping (SLAM) method to improve robotic perception in underground environments. The new approach boosts visual fidelity and mapping accuracy, enabling better digital twin development.

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    Area of Science:

    • Robotics and Autonomous Systems
    • Computer Vision
    • Geospatial Information Science

    Background:

    • Efficient underground space utilization is critical for mitigating land scarcity.
    • 3D Gaussian Splatting (3DGS) offers advantages for robotic perception and digitalization in underground environments.
    • Existing Simultaneous Localization and Mapping (SLAM) systems struggle with accuracy due to poor illumination, sensor noise, and geometric degradation in underground spaces.

    Purpose of the Study:

    • To develop an RGB-D perception-enhanced 3DGS SLAM method for robust underground localization and mapping.
    • To address challenges posed by low-light conditions, noise, and geometric degradation in underground environments.
    • To improve the accuracy and efficiency of SLAM systems for creating digital twins of underground spaces.

    Main Methods:

    • Implemented a data enhancement pipeline using Multi-Scale Retinex with Color Restoration (MSRCR), Side Window Filtering (SWF), and adaptive gamma correction in the HIS color space.
    • Developed a depth completion network for hole-filling and a hybrid metric for keyframe selection balancing efficiency and completeness.
    • Introduced a dual-constraint Gaussian management strategy with opacity threshold and observation frequency, coupled with loop closure detection for global consistency.

    Main Results:

    • Achieved a 13.8% improvement in Peak Signal-to-Noise Ratio (PSNR) compared to state-of-the-art benchmarks.
    • Demonstrated competitive trajectory accuracy and computational efficiency in experiments within coal mine tunnels and underground parking garages.
    • Validated the method's effectiveness using a custom-designed underground mobile robot platform.

    Conclusions:

    • The proposed RGB-D perception-enhanced 3DGS SLAM method significantly improves localization and mapping robustness in challenging underground environments.
    • The data enhancement, keyframe selection, and Gaussian management strategies effectively address illumination variations and sensor degradation.
    • The findings strongly support the advancement of digital twin systems for underground spaces, enhancing their practical applicability.