Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization
The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, sem...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/16/6263 |
| _version_ | 1797431692642746368 |
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| author | Chao Li Wennan Chai Xiaohui Yang Qingdang Li |
| author_facet | Chao Li Wennan Chai Xiaohui Yang Qingdang Li |
| author_sort | Chao Li |
| collection | DOAJ |
| description | The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map. |
| first_indexed | 2024-03-09T09:50:00Z |
| format | Article |
| id | doaj.art-68ba7843d1ff4507bdf31d919392a91b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:50:00Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-68ba7843d1ff4507bdf31d919392a91b2023-12-02T00:17:42ZengMDPI AGSensors1424-82202022-08-012216626310.3390/s22166263Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph OptimizationChao Li0Wennan Chai1Xiaohui Yang2Qingdang Li3College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, ChinaCollege of Sino-German Institute Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, ChinaFaculty of Electrical Engineering and Computer Science, University of Kassel, 34132 Kassel, GermanyCollege of Sino-German Institute Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, ChinaThe advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map.https://www.mdpi.com/1424-8220/22/16/6263crowdsourcinggraph optimizationlocalizationmappingmulti-sensor fusionobject detector |
| spellingShingle | Chao Li Wennan Chai Xiaohui Yang Qingdang Li Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization Sensors crowdsourcing graph optimization localization mapping multi-sensor fusion object detector |
| title | Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization |
| title_full | Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization |
| title_fullStr | Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization |
| title_full_unstemmed | Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization |
| title_short | Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization |
| title_sort | crowdsourcing based indoor semantic map construction and localization using graph optimization |
| topic | crowdsourcing graph optimization localization mapping multi-sensor fusion object detector |
| url | https://www.mdpi.com/1424-8220/22/16/6263 |
| work_keys_str_mv | AT chaoli crowdsourcingbasedindoorsemanticmapconstructionandlocalizationusinggraphoptimization AT wennanchai crowdsourcingbasedindoorsemanticmapconstructionandlocalizationusinggraphoptimization AT xiaohuiyang crowdsourcingbasedindoorsemanticmapconstructionandlocalizationusinggraphoptimization AT qingdangli crowdsourcingbasedindoorsemanticmapconstructionandlocalizationusinggraphoptimization |