HeadSLAM: Pedestrian SLAM with head-mounted sensors
Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is on...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
MDPI
2022
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| _version_ | 1826263400127135744 |
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| author | Hou, X Bergmann, J |
| author_facet | Hou, X Bergmann, J |
| author_sort | Hou, X |
| collection | OXFORD |
| description | Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation. |
| first_indexed | 2024-03-06T19:51:09Z |
| format | Journal article |
| id | oxford-uuid:2405153a-b8cc-44b8-bf59-50de7e42f41e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:51:09Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oxford-uuid:2405153a-b8cc-44b8-bf59-50de7e42f41e2022-03-26T11:47:35ZHeadSLAM: Pedestrian SLAM with head-mounted sensorsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2405153a-b8cc-44b8-bf59-50de7e42f41eEnglishSymplectic ElementsMDPI2022Hou, XBergmann, JResearch focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation. |
| spellingShingle | Hou, X Bergmann, J HeadSLAM: Pedestrian SLAM with head-mounted sensors |
| title | HeadSLAM: Pedestrian SLAM with head-mounted sensors |
| title_full | HeadSLAM: Pedestrian SLAM with head-mounted sensors |
| title_fullStr | HeadSLAM: Pedestrian SLAM with head-mounted sensors |
| title_full_unstemmed | HeadSLAM: Pedestrian SLAM with head-mounted sensors |
| title_short | HeadSLAM: Pedestrian SLAM with head-mounted sensors |
| title_sort | headslam pedestrian slam with head mounted sensors |
| work_keys_str_mv | AT houx headslampedestrianslamwithheadmountedsensors AT bergmannj headslampedestrianslamwithheadmountedsensors |