Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model
Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Up...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 24; H. 4; S. 1073 |
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| Abstract | Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian’s motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation. |
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| AbstractList | Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian’s motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation. Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian's motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation.Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian's motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation. |
| Audience | Academic |
| Author | Liu, Gang Guo, Meifeng Chen, Jianqiang |
| AuthorAffiliation | 2 Department of Electronic Engineering, Tsinghua University, Beijing 100084, China 1 Department of Precision Instrument, Tsinghua University, Beijing 100084, China; chenjq16@g.ecc.u-tokyo.ac.jp (J.C.); guomf@tsinghua.edu.cn (M.G.) |
| AuthorAffiliation_xml | – name: 1 Department of Precision Instrument, Tsinghua University, Beijing 100084, China; chenjq16@g.ecc.u-tokyo.ac.jp (J.C.); guomf@tsinghua.edu.cn (M.G.) – name: 2 Department of Electronic Engineering, Tsinghua University, Beijing 100084, China |
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| SubjectTerms | Accelerometers Accuracy Algorithms Analysis Attitudes Biomechanical Phenomena bipedal constraint algorithm Deep learning Electronics in navigation Foot Gait human step length model Humans Hypothesis testing INS Kinematics MIMU Motion Motion capture Navigation systems pedestrian navigation Pedestrians Radio frequency identification Sensors ZUPT |
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| Title | Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model |
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