Pedestrian Inertial Navigation Based on Full-Phase Constraints of Lower Limb Kinematics

Wang, Jianyu; Xu, Xiangbo; Liu, Jinhao

doi:10.1109/tim.2022.3198444

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Because low-cost MEMS IMUs are plagued by high sensor noise and bias, error accumulates rapidly during double integration. A commonly used solution is ZUPT [8][9][10], where IMU is mounted on a pedestrian's foot. By using the constraint of zero velocity when the foot touches the ground, Kalman filtering is utilized to compensate for error.…”

Section: Traditional Methodsmentioning

confidence: 99%

“…Therefore, it is necessary to tackle the high sensor noise for a robust and precise inertial navigation system. To compensate for the error and improve localization accuracy without the aid of other sensors, traditional methods rely on human motion laws and special constraints to reduce the cumulative error, such as Zero velocity UPdaTe (ZUPT) [8][9][10], Zero Angular Rate Update (ZARU) [11] and step-based pedestrian dead reckoning methods (PDR) [12,13]. However, these methods demand IMU is fixed to a special position for a pedestrian, such as a foot, and are only suitable for periodic pedestrian motion.…”

Section: Introductionmentioning

confidence: 99%

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Neural inertial navigation system on pedestrian

Huang

Gao

Liu

et al. 2023

Meas. Sci. Technol.

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The recent research shows that data-driven inertia navigation technology can significantly alleviate the drift error of Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS-IMU) in pedestrian localization. However, most existing methods must rely on attitude information provided by external procedure (such as smartphone API), which violates the original intention of full autonomy of inertial navigation, and attitude information is also inaccurate. To address the problem, we propose a pedestrian indoor neural inertial navigation system that does not rely on external information and is only based on low-cost MEMS-IMU. First, a deep learning based neural inertial network was designed to estimate attitude. Then, in order to obtain position estimation with both global and local accuracy, an Invariant Extended Kalman Filter (IEKF) framework was proposed, where 3D displacement and its uncertainty regressed by a deep residual network are utilized to update IEKF. Extensive experimental results on a public dataset and a self-collected dataset show that the proposed method provides accurate attitude estimation and outperforms state-of-the-art methods in position estimation, demonstrating the superiority of our method in reliability and accuracy.

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Section: Traditional Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural inertial navigation system on pedestrian

Huang

Gao

Liu

et al. 2023

Meas. Sci. Technol.

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show abstract

“…In firefighting, localization using the existing infrastructure may be infeasible, because a large number of sensor nodes should be arranged in advance [4,5]. Therefore, autonomous navigation, such as inertial navigation, is preferred [6].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Localization of Firefighters Based on Relative Ranging Constraints of UWB and Autonomous Navigation

Chong

Guo

et al. 2023

Electronics

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There are many demands for the cooperative localization (CL) of multiple people, such as firefighter rescue. The classical foot-mounted inertial navigation based on zero velocity update (ZUPT) suffers from accumulating error due to the low-cost inertial sensor, and the pre-placed anchors in the ultra-wideband (UWB) system limit the application in an unknown environment. In this study, a group of sensors including the inertial measurement unit (IMU), magnetometer, barometer, and UWB sensor is used. Through the different characteristics of sensors and the position relationship between people, a cooperative localization system using an extended Kalman filter for three-dimensional firefighter tracking is proposed. Ranging information between firefighters from UWB is utilized, and couplings introduced by relative measurement are estimated. Two experiments are designed to verify the proposed algorithm in building and forest environments. Compared with the results of single-person inertial navigation, the average positioning precision of the algorithm in the building and forest is, respectively, improved by 38.93% and 79.01%. This approach successfully suppresses the divergence of positioning errors, and fixed UWB anchors are not needed.

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Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing

Wang

Zhang

et al. 2022

Advanced Engineering Informatics

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Pedestrian Inertial Navigation Based on Full-Phase Constraints of Lower Limb Kinematics

Cited by 7 publications

References 32 publications

Neural inertial navigation system on pedestrian

Neural inertial navigation system on pedestrian

Cooperative Localization of Firefighters Based on Relative Ranging Constraints of UWB and Autonomous Navigation

Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing

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