Indoor Navigation Elements Extraction of Room Fineness Using Refining Space Separator Method

LIU Minglei; WEI Shuangfeng; HUANG Shuai; TANG Nian

doi:10.13203/j.whugis20190223

Volume 46 Issue 2

Jan. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(2): 221-229. > DOI: 10.13203/j.whugis20190223

LIU Minglei, WEI Shuangfeng, HUANG Shuai, TANG Nian. Indoor Navigation Elements Extraction of Room Fineness Using Refining Space Separator Method[J]. Geomatics and Information Science of Wuhan University, 2021, 46(2): 221-229. DOI: 10.13203/j.whugis20190223

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Indoor Navigation Elements Extraction of Room Fineness Using Refining Space Separator Method

LIU Minglei^1,,
WEI Shuangfeng^{1, 2, 3, 4, ,},
HUANG Shuai¹,
TANG Nian¹

1.
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
2.
Engineering Research Center of Representative Building and Architectural Heritage Database, Ministry of Education, Beijing 102616, China
3.
Key Laboratory for Urban Spatial Informatics of Ministry of Natural Resources, Beijing 102616, China
4.
Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, China

Funds:

The National Key Research and Development Program of China 2016YFC0702107

he Postgraduate Innovation Research Project of Beijing University of Civil Engineering and Architecture PG2019058

he Postgraduate Innovation Research Project of Beijing University of Civil Engineering and Architecture PG2019061

he Postgraduate Innovation Research Project of Beijing University of Civil Engineering and Architecture PG2019065

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Author Bio:
LIU Minglei, master, specializes in 3D indoor navigation modeling based on point cloud. E-mail: Liuminglei1009@163.com
Corresponding author:
WEi Shuangfeng, PhD, associate professor. E-mail:weishuangfeng@bucea.edu.cn
Received Date: December 06, 2019
Published Date: February 04, 2021

Graphical Abstract

Abstract

Abstract

In the existing methods of indoor 3D model reconstruction, indoor navigation elements that work as space separators are usually regarded as undividable structure. However, the shape difference between two wall surfaces on one wall will cause details loss in the indoor 3D reconstruction room extraction, as well as difficulties in extracting doors and windows. Aiming to solve this problem, this paper proposes an idea of refining space separator. By refining one wall into two wall surfaces, regional growth algorithm is applied to obtain the corner points of inner wall, so that the refined expression of the interior can be obtained. Point cloud densities of corresponding areas on two wall surfaces are compared to avoid the influence of the obstacles blocking the wall surface on the extraction result of door and window extraction. The results show that the proposed method can effectively extract indoor doors and windows, which provides an important basis for the generation of navigation network.
- point cloud,
- indoor navigation elements,
- refined space separator,
- regional growth algorithm

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References

[1]	朱庆, 胡明远, 许伟平, 等.面向火灾动态疏散的三维建筑信息模型[J].武汉大学学报·信息科学版, 2014, 39(7):762-766 http://ch.whu.edu.cn/article/id/3016 Zhu Qing, Hu Mingyuan, Xu Weiping, et al. 3D Building Information Model for Facilitating Dynamic Analysis of Indoor Fire Emergency[J]. Geomatics and Information Science of Wuhan University, 2014, 39(7):762-766 http://ch.whu.edu.cn/article/id/3016
[2]	韩丽娜, 李缘廷.融合地面激光扫描和摄影测量技术的三维重建[J].测绘地理信息, 2015, 40(3):10-12 Han Lina, Li Yuanting. 3D Reconstruction by Combining Terrestrial Laser Scanner Data and Photogrammetric Images[J]. Journal of Geomatics, 2015, 40(3):10-12
[3]	张春森, 张梦辉, 郭丙轩, 等.影像三维重建的网格自适应快速优化[J].武汉大学学报·信息科学版, 2020, 45(3): 411-418 doi: 10.13203/j.whugis20190161 Zhang Chunsen, Zhang Menghui, Guo Bingxuan, et al. Adaptive Fast Mesh Refinement of 3D Reconstruction Based on Image Information[J]. Geomatics and Information Science of Wuhan University, 2020, 45(3): 411-418 doi: 10.13203/j.whugis20190161
[4]	危双丰, 刘明蕾, 赵江洪, 等.利用点云检测室内导航元素的方法综述[J].武汉大学学报·信息科学版, 2018, 43(12): 2 003-2 011 doi: 10.13203/j.whugis20180144 Wei Shuangfeng, Liu Minglei, Zhao Jianghong, et al. A Survey of Methods for Detecting Indoor Navigation Elements from Point Clouds[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2 003-2 011 doi: 10.13203/j.whugis20180144
[5]	Fichtner F W, Diakité A A, Zlatanova S, et al. Semantic Enrichment of Octree Structured Point Clouds for Multi-story 3D Pathfinding[J]. Transactions in GIS, 2018, 22: 233-248 doi: 10.1111/tgis.12308
[6]	Thomson C, Boehm J. Automatic Geometry Generation from Point Clouds for BIMs[J]. Remote Sensing, 2015, 7(9):11 753-11 775 doi: 10.3390/rs70911753
[7]	Zhang J, Duan M, Yan Q, et al. Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method[J]. Remote Sensing, 2014, 6(9):8 405-8 423 doi: 10.3390/rs6098405
[8]	Anagnostopoulos I, Pătrăucean V, Brilakis I, et al. Detection of Walls, Floors, and Ceilings in Point Cloud Data[J]. Construction Research Congress, 2016, DOI: 10.1061/9780784479827.229
[9]	Ochmann S, Vock R, Wessel R, et al. Automatic Generation of Structural Building Descriptions from 3D Point Cloud Scans[C]. 2014 International Conference on Computer Graphics Theory and Applications (GRAPP), Lisbon, Portugal, 2014
[10]	Schnabel R, Wahl R, Klein R. Efficient RANSAC for Point‐Cloud Shape Detection[J]. Computer Graphics Forum, 2007, 26(2):214-226 doi: 10.1111/j.1467-8659.2007.01016.x
[11]	Díaz-Vilariño L, Khoshelham K, Martínez-Sánchez J, et al. 3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds[J]. Sensors, 2015, 15(2):3 491-3 512 doi: 10.3390/s150203491
[12]	Díaz-Vilariño L, Boguslawski P, Khoshelham K, et al. Indoor Navigation from Point Clouds: 3D Modeling and Obstacle Detection[J]. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B4:275-281
[13]	Armeni I, Sener O, Zamir A R, et al. 3D Semantic Parsing of Large-Scale Indoor Spaces[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016
[14]	Armeni I, Sener O, Zamir A R, et al. 3D Semantic Parsing of Large-Scale Indoor Spaces Supplementary Material[EB/OL]. http://cvgl.stanford.edu/papers/iro_cvpr16_supp.pdf, 2018
[15]	Oesau S, Lafarge F, Alliez P. Indoor Scene Reconstruction Using Feature Sensitive Primitive Extraction and Graph-cut[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2014, 90:68-82
[16]	Sanchez V, Zakhor A. Planar 3D Modeling of Building Interiors from Point Cloud Data[C]. IEEE International Conference on Image Processing, Orlando, FL, USA, 2012
[17]	Gurram P, Hu S, Chan A. Uniform Grid Upsampling of 3D LiDAR Point Cloud Data[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2013, DOI: 10.1117/12.2004200
[18]	Grompone V G R, Jakubowicz J, Morel J M, et al. LSD: A Fast Line Segment Detector with a False Detection Control[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010, 32(4):722-732
[19]	Angelina S, Suresh L P, Veni S H K. Image Segmentation Based on Genetic Algorithm for Region Growth and Region Merging[C]. International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Kumaracoil, India, 2012
[20]	游天, 王光霞, 吕晓华, 等.一种面向室内导航的通行区域模型及其自动提取算法[J].武汉大学学报·信息科学版, 2019, 44(2): 177-184 doi: 10.13203/j.whugis20170056 You Tian, Wang Guangxia, Lü Xiaohua, et al. Traversable Region Model and Its Automatic Extraction Algorithm for Indoor Navigation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(2): 177-184 doi: 10.13203/j.whugis20170056

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