Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map
Recently, the map matching-assisted positioning method based on micro-electromechanical systems (MEMS) inertial devices has become a research hotspot for indoor pedestrian positioning; however, these are based on existing indoor electronic maps. In this paper, without prior knowledge of the map and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-666X/9/6/267 |
| _version_ | 1819084386556968960 |
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| author | Jingjing Shi Mingrong Ren Pu Wang Juan Meng |
| author_facet | Jingjing Shi Mingrong Ren Pu Wang Juan Meng |
| author_sort | Jingjing Shi |
| collection | DOAJ |
| description | Recently, the map matching-assisted positioning method based on micro-electromechanical systems (MEMS) inertial devices has become a research hotspot for indoor pedestrian positioning; however, these are based on existing indoor electronic maps. In this paper, without prior knowledge of the map and through building an indoor main path feature point map combined with the simultaneous localization and map building (SLAM) particle filter (PF-SLAM) algorithm idea, a PF-SLAM indoor pedestrian location algorithm based on a feature point map was proposed through the inertial measurement unit to improve indoor pedestrian positioning accuracy. Aiming at the problem of inaccurate heading angle estimation in the pedestrian dead reckoning (PDR) algorithm, a turn-straight-state threshold detection method was proposed that corrected the difference of the heading angles during the straight-line walking of pedestrians to suppress the error accumulation of the heading angle. Aiming at the particles that are severely divergent at the corners, a feature point matching algorithm was proposed to correct the pedestrian position error. Furthermore, the turning point extracted the main path that failed to match the current feature point map as a new feature point was added to update the map. Through the mutual modification of SLAM and an inertial navigation system (INS) the long-time, high-precision, and low-cost positioning functions of indoor pedestrians were realized. |
| first_indexed | 2024-12-21T20:47:38Z |
| format | Article |
| id | doaj.art-98471abf3e244d75bec26ad1bef1d1a7 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-12-21T20:47:38Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-98471abf3e244d75bec26ad1bef1d1a72022-12-21T18:50:47ZengMDPI AGMicromachines2072-666X2018-05-019626710.3390/mi9060267mi9060267Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point MapJingjing Shi0Mingrong Ren1Pu Wang2Juan Meng3College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCollege of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCollege of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCollege of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaRecently, the map matching-assisted positioning method based on micro-electromechanical systems (MEMS) inertial devices has become a research hotspot for indoor pedestrian positioning; however, these are based on existing indoor electronic maps. In this paper, without prior knowledge of the map and through building an indoor main path feature point map combined with the simultaneous localization and map building (SLAM) particle filter (PF-SLAM) algorithm idea, a PF-SLAM indoor pedestrian location algorithm based on a feature point map was proposed through the inertial measurement unit to improve indoor pedestrian positioning accuracy. Aiming at the problem of inaccurate heading angle estimation in the pedestrian dead reckoning (PDR) algorithm, a turn-straight-state threshold detection method was proposed that corrected the difference of the heading angles during the straight-line walking of pedestrians to suppress the error accumulation of the heading angle. Aiming at the particles that are severely divergent at the corners, a feature point matching algorithm was proposed to correct the pedestrian position error. Furthermore, the turning point extracted the main path that failed to match the current feature point map as a new feature point was added to update the map. Through the mutual modification of SLAM and an inertial navigation system (INS) the long-time, high-precision, and low-cost positioning functions of indoor pedestrians were realized.http://www.mdpi.com/2072-666X/9/6/267indoor localizationinertial navigation system (INS)simultaneous localization and map building (SLAM) algorithmparticle filteringfeature point matching |
| spellingShingle | Jingjing Shi Mingrong Ren Pu Wang Juan Meng Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map Micromachines indoor localization inertial navigation system (INS) simultaneous localization and map building (SLAM) algorithm particle filtering feature point matching |
| title | Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map |
| title_full | Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map |
| title_fullStr | Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map |
| title_full_unstemmed | Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map |
| title_short | Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map |
| title_sort | research on pf slam indoor pedestrian localization algorithm based on feature point map |
| topic | indoor localization inertial navigation system (INS) simultaneous localization and map building (SLAM) algorithm particle filtering feature point matching |
| url | http://www.mdpi.com/2072-666X/9/6/267 |
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