A Novel Indoor Structure Extraction Based on Dense Point Cloud
Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/11/660 |
| _version_ | 1797549074701877248 |
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| author | Pengcheng Shi Qin Ye Lingwen Zeng |
| author_facet | Pengcheng Shi Qin Ye Lingwen Zeng |
| author_sort | Pengcheng Shi |
| collection | DOAJ |
| description | Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures. |
| first_indexed | 2024-03-10T15:08:31Z |
| format | Article |
| id | doaj.art-e6871560cef84a7581ede5be9f4b324f |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T15:08:31Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-e6871560cef84a7581ede5be9f4b324f2023-11-20T19:33:21ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-11-0191166010.3390/ijgi9110660A Novel Indoor Structure Extraction Based on Dense Point CloudPengcheng Shi0Qin Ye1Lingwen Zeng2College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaHerein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.https://www.mdpi.com/2220-9964/9/11/660indoor structure extractionfeature structure mapplane segmentationfeature triangulationnavigation structure mappassable area map |
| spellingShingle | Pengcheng Shi Qin Ye Lingwen Zeng A Novel Indoor Structure Extraction Based on Dense Point Cloud ISPRS International Journal of Geo-Information indoor structure extraction feature structure map plane segmentation feature triangulation navigation structure map passable area map |
| title | A Novel Indoor Structure Extraction Based on Dense Point Cloud |
| title_full | A Novel Indoor Structure Extraction Based on Dense Point Cloud |
| title_fullStr | A Novel Indoor Structure Extraction Based on Dense Point Cloud |
| title_full_unstemmed | A Novel Indoor Structure Extraction Based on Dense Point Cloud |
| title_short | A Novel Indoor Structure Extraction Based on Dense Point Cloud |
| title_sort | novel indoor structure extraction based on dense point cloud |
| topic | indoor structure extraction feature structure map plane segmentation feature triangulation navigation structure map passable area map |
| url | https://www.mdpi.com/2220-9964/9/11/660 |
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