GS-SLAM：基于语义增强与3D高斯溅射的实时SLAM系统

刘雨晴; 王峰; 韩晓明; 宋立瑞; 李泽梁

doi:10.12382/bgxb.2025.1103

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GS-SLAM：基于语义增强与3D高斯溅射的实时SLAM系统

更新时间：2026-07-31

- GS-SLAM：基于语义增强与3D高斯溅射的实时SLAM系统
- GS-SLAM: a Real-time SLAM System Based on Semantic Enhancement and 3D Gaussian Splatting
- 兵工学报 2026年
- 作者机构：
  
  太原理工大学电气与动力工程学院,山西,太原,030024
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.1103
  中图分类号： TP391.41
- 收稿：2025-12-16，
  
  网络首发：2026-07-31，
- 稿件说明：
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刘雨晴，王峰，韩晓明，等. GS-SLAM：基于语义增强与3D高斯溅射的实时SLAM系统[J/OL]. 兵工学报, 2026(2026-07-31). https://doi.org/10.12382/bgxb.2025.1103.

LIU Y Q, WANG F, HAN X M, et al. Gs-slam: a real-time slam system based on semantic enhancement and 3d gaussian splatting[J/OL]. Acta Armamentarii, 2026(2026-07-31). https://doi.org/10.12382/bgxb.2025.1103. (in Chinese)
刘雨晴，王峰，韩晓明，等. GS-SLAM：基于语义增强与3D高斯溅射的实时SLAM系统[J/OL]. 兵工学报, 2026(2026-07-31). https://doi.org/10.12382/bgxb.2025.1103. DOI：

LIU Y Q, WANG F, HAN X M, et al. Gs-slam: a real-time slam system based on semantic enhancement and 3d gaussian splatting[J/OL]. Acta Armamentarii, 2026(2026-07-31). https://doi.org/10.12382/bgxb.2025.1103. (in Chinese) DOI：

摘要

为了提升动态战场环境下无人平台的定位鲁棒性、场景重建质量与实时渲染能力。针对传统实时定位与建图（Simultaneous Localization and Mapping，SLAM）算法在动态环境中易出现定位漂移，以及稀疏点云或体素地图难以支持高质量3D可视化与渲染的双重挑战，提出一种融合鲁棒动态语义前端与3D高斯溅射建图后端的实时语义SLAM系统，GS-SLAM。前端采用基于改进MobileNetV2骨干网络的轻量化SOLOv2算法，实现实时语义分割；在此基础上，提出三帧极线一致性约束与退化场景下的双阈值几何约束，构建基于恒速运动假设与3D轮廓重投影的漏检补偿机制，实现对动态特征点的有效剔除。后端设计独立异步3D高斯建图线程，提出滑动窗口一致性动态掩码融合策略，对多帧语义和几何检测结果进行时序统计；构建融合匹配点数量与跟踪稳定性的自适应优选机制，筛选高质量关键帧，在动态场景中构建静态3D高斯模型，实现实时处理与高质量渲染。实验结果表明，在TUM数据集的高动态序列上，相比原始ORB-SLAM2，该算法的绝对轨迹误差显著降低（降幅最高超过99%），相对位姿误差降幅均达93%以上；在Bonn数据集上亦取得较优结果；相比同类先进算法具有更优的定位精度。

Abstract

This paper aims to enhance the localization robustness

scene reconstruction quality and real-time rendering capability of unmanned platforms in dynamic battlefield environments.To address the dual challengesoftraditionalsimultaneouslocalization andmapping(SLAM) algorithmsbeingprone to localization drift in dynamic environments anddifficultysupportinghigh-quality 3D visualization and renderingwithsparse point clouds or voxel maps

a real-time semantic SLAM system is proposed. This system integrates a robust dynamic semantic front-end with a 3D Gaussiansplatting mapping back-end.In the front-end

a lightweight SOLOv2 algorithm based on an improved MobileNetV2 backbonenetwork is used toachieve real-time semantic segmentation. To effectively eliminatethedynamic feature points

this paperproposesa three-frame epipolar consistency constraint and a dual-threshold geometric constraint for degraded scenes. These are supplemented by a missed detection compensation mechanism based on a constant velocity motion assumption and 3D contour reprojection.An independent asynchronous 3D Gaussian mapping threadis designed in theback-end.Asliding window consistency dynamic mask fusion strategyis proposed to conduct thetemporal statistical analysis onthemulti-frame semantic and geometric detection results. Furthermore

an adaptive selection mechanism based on the number of matched points and tracking stability is introduced to filter high-quality keyframes

thereby constructing a static 3D Gaussian model within dynamic scenes. Experimental results on the TUM dataset demonstrate that

compared to the original ORB-SLAM2

the proposed algorithm significantly reduces theabsolutetrajectoryerror (ATE) with a maximum reductionof more than99%

and consistently reduces therelativeposeerror (RPE) by over 93% in high-dynamic scenarios. Significant improvementsin trajectory estimation accuracyare also observed on the Bonn dataset.The proposed algorithm exhibitshassuperior localization accuracycompared tosimilarstate-of-the-art algorithms. Additionally

the systemrealizes real-time processing and high-quality rendering.

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references

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