Research on Visual SLAM Systems in Indoor Dynamic Scenes.
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| Title: | Research on Visual SLAM Systems in Indoor Dynamic Scenes. |
|---|---|
| Authors: | Yanhui Lv1 yanhuilv@126.com, Qiang Zhong2 1638683463@qq.com |
| Source: | Engineering Letters. Jan2026, Vol. 34 Issue 1, p164-174. 11p. |
| Subjects: | Visual odometry, Image segmentation, Statistical reliability |
| Abstract: | To address the issue of mobile objects in indoor dynamic scenes degrading the localization accuracy of SLAM systems, we propose a visual SLAM system with dynamic point elimination based on ORB-SLAM3. First, an instance segmentation model is designed using YOLOv8-seg, incorporating a GhostBottleneck module to achieve model lightweighting, and introducing the SimAM attention mechanism to enhance segmentation performance. Second, using instance segmentation models to assist SLAM systems in feature extraction, and combining geometric constraint methods to remove dynamic points, in order to improve the pose estimation accuracy of SLAM systems. Finally, a dense point cloud mapping module is developed, where dynamic-area point clouds are filtered using segmentation results, followed by statistical and voxel filtering for optimization. Experimental results demonstrate that compared to ORB-SLAM3, the proposed system reduces the average RMSE of absolute trajectory error by 47.135%. Against other classical SLAM systems, it exhibits superior dynamic point removal capability in dynamic environments and effectively mitigates the impact of dynamic objects on dense point cloud map reconstruction. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 190639036 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on Visual SLAM Systems in Indoor Dynamic Scenes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yanhui+Lv%22">Yanhui Lv</searchLink><relatesTo>1</relatesTo><i> yanhuilv@126.com</i><br /><searchLink fieldCode="AR" term="%22Qiang+Zhong%22">Qiang Zhong</searchLink><relatesTo>2</relatesTo><i> 1638683463@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jan2026, Vol. 34 Issue 1, p164-174. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Visual+odometry%22">Visual odometry</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the issue of mobile objects in indoor dynamic scenes degrading the localization accuracy of SLAM systems, we propose a visual SLAM system with dynamic point elimination based on ORB-SLAM3. First, an instance segmentation model is designed using YOLOv8-seg, incorporating a GhostBottleneck module to achieve model lightweighting, and introducing the SimAM attention mechanism to enhance segmentation performance. Second, using instance segmentation models to assist SLAM systems in feature extraction, and combining geometric constraint methods to remove dynamic points, in order to improve the pose estimation accuracy of SLAM systems. Finally, a dense point cloud mapping module is developed, where dynamic-area point clouds are filtered using segmentation results, followed by statistical and voxel filtering for optimization. Experimental results demonstrate that compared to ORB-SLAM3, the proposed system reduces the average RMSE of absolute trajectory error by 47.135%. Against other classical SLAM systems, it exhibits superior dynamic point removal capability in dynamic environments and effectively mitigates the impact of dynamic objects on dense point cloud map reconstruction. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190639036 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 164 Subjects: – SubjectFull: Visual odometry Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Statistical reliability Type: general Titles: – TitleFull: Research on Visual SLAM Systems in Indoor Dynamic Scenes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yanhui Lv – PersonEntity: Name: NameFull: Qiang Zhong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 1 Titles: – TitleFull: Engineering Letters Type: main |
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