Gaussian Splatting SLAM Based on Loop Closure Pose Optimization and Comprehensive Loss Function

With their high-fidelity scene representation capabilities, Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have deeply attracted attention in Simultaneous Localization and Mapping (SLAM) community. This paper proposes a 3DGS-based SLAM method that integrates the loop closure detectio...

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Bibliographic Details
Published inIEEE International Conference on Robotics and Biomimetics (Online) pp. 2131 - 2136
Main Authors Wang, Dezhi, Wu, Xinzhao, Zhang, Liwei, Tu, Degui
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.12.2024
Subjects
Accuracy
Neural radiance field
Optimization
Real-time systems
Robots
Simultaneous localization and mapping
Three-dimensional displays
Tracking loops
Urban areas
Virtual environments
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Summary:With their high-fidelity scene representation capabilities, Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have deeply attracted attention in Simultaneous Localization and Mapping (SLAM) community. This paper proposes a 3DGS-based SLAM method that integrates the loop closure detection module and designs a comprehensive loss function. Graphical optimizations are used to optimize trace threads and correct for accumulated errors. The experimental results show that the average improvement is about 54% compared with SplaTAM in terms of tracking, and the image similarity is also improved in terms of mapping.
ISSN:2994-3574
DOI:10.1109/ROBIO64047.2024.10907594