Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering
To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object mas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/7/236 |
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| author | Yiming Li Luying Na Xianpu Liang Qi An |
| author_facet | Yiming Li Luying Na Xianpu Liang Qi An |
| author_sort | Yiming Li |
| collection | DOAJ |
| description | To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning. |
| format | Article |
| id | doaj-art-b1ba8640ff7e4aa59116530bdd51611a |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-b1ba8640ff7e4aa59116530bdd51611a2025-08-20T02:45:46ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-06-0114723610.3390/ijgi14070236Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical FilteringYiming Li0Luying Na1Xianpu Liang2Qi An3Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, ChinaMechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, ChinaMechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, ChinaState Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaTo address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning.https://www.mdpi.com/2220-9964/14/7/236visual SLAMdynamic environment mappingsemantic fusionhierarchical filteringsemantic maps2D grid maps |
| spellingShingle | Yiming Li Luying Na Xianpu Liang Qi An Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering ISPRS International Journal of Geo-Information visual SLAM dynamic environment mapping semantic fusion hierarchical filtering semantic maps 2D grid maps |
| title | Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering |
| title_full | Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering |
| title_fullStr | Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering |
| title_full_unstemmed | Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering |
| title_short | Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering |
| title_sort | indoor dynamic environment mapping based on semantic fusion and hierarchical filtering |
| topic | visual SLAM dynamic environment mapping semantic fusion hierarchical filtering semantic maps 2D grid maps |
| url | https://www.mdpi.com/2220-9964/14/7/236 |
| work_keys_str_mv | AT yimingli indoordynamicenvironmentmappingbasedonsemanticfusionandhierarchicalfiltering AT luyingna indoordynamicenvironmentmappingbasedonsemanticfusionandhierarchicalfiltering AT xianpuliang indoordynamicenvironmentmappingbasedonsemanticfusionandhierarchicalfiltering AT qian indoordynamicenvironmentmappingbasedonsemanticfusionandhierarchicalfiltering |