Article(id=1262079398791004439, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519407, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752768000000, receivedDateStr=2025-07-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832776973, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832776973, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832776973, creator=13701087609, updateTime=1778832776973, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=110, endPage=122, ext={EN=ArticleExt(id=1262079400934293800, articleId=1262079398791004439, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=SNGO-SLAM algorithm for complex indoor environments based on 3D Gaussian splatting, columnId=1262079399856300214, journalTitle=Electronic Measurement Technology, columnName=Theory and Algorithms, runingTitle=null, highlight=null, articleAbstract=

In recent years, the application of 3D Gaussian splatting technology in simultaneous localization and mapping systems has made it possible to perform high-quality image rendering using explicit 3D Gaussian models, significantly improving the fidelity of environmental reconstruction. However, the existing methods based on 3DGS have problems such as limited tracking accuracy and lack of global consistency in the 3D reconstruction of complex indoor environments. For this purpose, this paper proposes a dense SLAM algorithm based on 3D Gaussian splatting—SNGO-SLAM. This algorithm combines the advantages of both frame-to-model and frame-to-frame tracking methods, and uses surface normal perception to obtain richer geometric information, significantly improving the tracking accuracy. To address the tracking error that occurs over time, the algorithm introduces a loop closure process and optimizes the 3D Gaussian point representation problem, further enhancing the tracking accuracy. In addition, this algorithm also introduces a dual Gaussian pruning strategy, optimizing memory usage and ensuring precise camera tracking. Experiments on the Replica, ScanNet and TUM RGBD datasets show that while maintaining high rendering quality, the absolute root mean square error of the trajectory of this algorithm on the Replica dataset reaches 0.27 cm. Compared with NICE SLAM, Vox-Fusion, Gaussian SLAM and SplaTAM, the tracking accuracy has increased by 74.53%, 91.26%, 12.90% and 28.95% respectively, providing new ideas for SLAM technology.

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近年来，3D高斯溅射技术在同步定位与建图系统中的应用使得利用显式三维高斯模型进行高质量图像渲染成为可能，显著提升了环境重建的保真度。然而，现有的基于3DGS的方法在复杂室内环境的三维重建中存在跟踪精度有限、缺乏全局一致性等问题。为此，提出了一种基于3D高斯溅射的密集SLAM算法——SNGO-SLAM。该算法结合帧到模型和帧到帧两种跟踪方法的优点，利用表面法线感知获得更丰富的几何信息，显著提升了跟踪精度。为了解决随时间推移产生的跟踪误差，算法设计了环路闭合过程，并优化了3D高斯点表示问题，进一步提高了跟踪精度。此外，该算法还引入了双重高斯修剪策略，优化了内存使用，确保了精确的相机跟踪。在Replica、ScanNet和TUM RGBD数据集上的实验表明，该算法在保持高渲染质量的同时，在Replica数据集上的绝对轨迹均方根误差达到了0.27 cm，与NICE-SLAM、Vox-Fusion、Gaussian-SLAM和SplaTAM相比，跟踪精度分别提高了74.53%、91.26%、12.90%和28.95%，为SLAM技术提供了新的思路。

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姜俊超，硕士研究生，主要研究方向为视觉SLAM、三维重建。E-mail: jiangjunchao0013@163.com

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姜俊超，硕士研究生，主要研究方向为视觉SLAM、三维重建。E-mail: jiangjunchao0013@163.com

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房建东，教授，硕士生导师，主要研究方向为机器人控制、机器人自主决策。E-mail: z1435562262@126.com

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房建东，教授，硕士生导师，主要研究方向为机器人控制、机器人自主决策。E-mail: z1435562262@126.com

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朱瑾，硕士研究生，主要研究方向为自动驾驶、目标检测与跟踪。E-mail: 20231800089@imut.edu.cn

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朱瑾，硕士研究生，主要研究方向为自动驾驶、目标检测与跟踪。E-mail: 20231800089@imut.edu.cn

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