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Article

A Novel Indoor Structure Extraction Based on Dense Point Cloud

by 1,2, 1,* and 1
1
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
2
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 660; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110660
Received: 15 September 2020 / Revised: 7 October 2020 / Accepted: 22 October 2020 / Published: 2 November 2020
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. View Full-Text
Keywords: indoor structure extraction; feature structure map; plane segmentation; feature triangulation; navigation structure map; passable area map; ground extraction indoor structure extraction; feature structure map; plane segmentation; feature triangulation; navigation structure map; passable area map; ground extraction
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MDPI and ACS Style

Shi, P.; Ye, Q.; Zeng, L. A Novel Indoor Structure Extraction Based on Dense Point Cloud. ISPRS Int. J. Geo-Inf. 2020, 9, 660. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110660

AMA Style

Shi P, Ye Q, Zeng L. A Novel Indoor Structure Extraction Based on Dense Point Cloud. ISPRS International Journal of Geo-Information. 2020; 9(11):660. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110660

Chicago/Turabian Style

Shi, Pengcheng, Qin Ye, and Lingwen Zeng. 2020. "A Novel Indoor Structure Extraction Based on Dense Point Cloud" ISPRS International Journal of Geo-Information 9, no. 11: 660. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110660

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