Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories
Lacking indoor navigation graph has become a bottleneck in indoor applications and services. This paper presents a novel automated indoor navigation graph reconstruction approach from large-scale low-frequency indoor trajectories without any other data sources. The proposed approach includes three s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/3/146 |
| _version_ | 1797412246148612096 |
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| author | Xin Fu Hengcai Zhang Peixiao Wang |
| author_facet | Xin Fu Hengcai Zhang Peixiao Wang |
| author_sort | Xin Fu |
| collection | DOAJ |
| description | Lacking indoor navigation graph has become a bottleneck in indoor applications and services. This paper presents a novel automated indoor navigation graph reconstruction approach from large-scale low-frequency indoor trajectories without any other data sources. The proposed approach includes three steps: trajectory simplification, 2D floor plan extraction and 3D navigation graph construction. First, we propose a ST-Join-Clustering algorithm to identify and simplify redundant stay points embedded in the indoor trajectories. Second, an indoor trajectory bitmap construction based on a self-adaptive Gaussian filter is developed, and we then propose a new improved thinning algorithm to extract 2D indoor floor plans. Finally, we present an improved CFSFDP algorithm with time constraints to identify the 3D topological connection points between two different floors. To illustrate the applicability of the proposed approach, we conducted a real-world case study using an indoor trajectory dataset of over 4000 indoor trajectories and 5 million location points. The case study results showed that the proposed approach improves the navigation network accuracy by 1.83% and the topological accuracy by 13.7% compared to the classical kernel density estimation approach. |
| first_indexed | 2024-03-09T05:00:08Z |
| format | Article |
| id | doaj.art-78bad9cd79934ec1a5190397a78654fc |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T05:00:08Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-78bad9cd79934ec1a5190397a78654fc2023-12-03T13:01:24ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-03-0110314610.3390/ijgi10030146Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing TrajectoriesXin Fu0Hengcai Zhang1Peixiao Wang2School of Water Conservancy and Environment, University of Jinan, Jinan 250022, ChinaState Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaLacking indoor navigation graph has become a bottleneck in indoor applications and services. This paper presents a novel automated indoor navigation graph reconstruction approach from large-scale low-frequency indoor trajectories without any other data sources. The proposed approach includes three steps: trajectory simplification, 2D floor plan extraction and 3D navigation graph construction. First, we propose a ST-Join-Clustering algorithm to identify and simplify redundant stay points embedded in the indoor trajectories. Second, an indoor trajectory bitmap construction based on a self-adaptive Gaussian filter is developed, and we then propose a new improved thinning algorithm to extract 2D indoor floor plans. Finally, we present an improved CFSFDP algorithm with time constraints to identify the 3D topological connection points between two different floors. To illustrate the applicability of the proposed approach, we conducted a real-world case study using an indoor trajectory dataset of over 4000 indoor trajectories and 5 million location points. The case study results showed that the proposed approach improves the navigation network accuracy by 1.83% and the topological accuracy by 13.7% compared to the classical kernel density estimation approach.https://www.mdpi.com/2220-9964/10/3/146indoor spacenavigation graphlow-frequency trajectorylocation-based services |
| spellingShingle | Xin Fu Hengcai Zhang Peixiao Wang Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories ISPRS International Journal of Geo-Information indoor space navigation graph low-frequency trajectory location-based services |
| title | Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories |
| title_full | Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories |
| title_fullStr | Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories |
| title_full_unstemmed | Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories |
| title_short | Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories |
| title_sort | automatic construction of indoor 3d navigation graph from crowdsourcing trajectories |
| topic | indoor space navigation graph low-frequency trajectory location-based services |
| url | https://www.mdpi.com/2220-9964/10/3/146 |
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