scholarly journals A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1578 ◽  
Author(s):  
Xiaochun Tian ◽  
Jiabin Chen ◽  
Yongqiang Han ◽  
Jianyu Shang ◽  
Nan Li
Keyword(s):  
Navigation System ◽  
Inertial Sensors ◽  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Interval

scholarly journals An Adaptive Zero Velocity Detection Algorithm Based on Multi-Sensor Fusion for a Pedestrian Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3261 ◽  
Author(s):  
Ming Ma ◽  
Qian Song ◽  
Yang Gu ◽  
Yanghuan Li ◽  
Zhimin Zhou
Keyword(s):  
Sensor Fusion ◽  
Navigation System ◽  
Detection Algorithm ◽  
Stair Climbing ◽  
Detection Methods ◽  
Recognition Method ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Motion Modes ◽  
Detection Approach

The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes.

Smooth estimation of human foot motion for zero-velocity-update-aided inertial pedestrian navigation system

Sensor Review ◽  
2015 ◽  
Vol 35 (4) ◽  
pp. 389-400 ◽  
Author(s):  
Hongyu Zhao ◽  
Zhelong Wang ◽  
Qin Gao ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi
Keyword(s):  
Navigation System ◽  
Gait Cycle ◽  
Inertial Sensor ◽  
Computational Cost ◽  
Content Type ◽  
Human Foot ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Foot Motion ◽  
Smooth Transitions

Purpose – The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system. Design/methodology/approach – The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data. Findings – Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation. Research limitations/implications – Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints. Practical implications – The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor. Originality/value – This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

scholarly journals An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yushuai Zhang ◽  
Jianxin Guo ◽  
Feng Wang ◽  
Rui Zhu ◽  
Liping Wang
Keyword(s):  
Indoor Localization ◽  
Inertial Navigation ◽  
Detection Algorithm ◽  
Position Estimation ◽  
Position Error ◽  
Indoor Navigation ◽  
Bayesian Filter ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

Pedestrian navigation system using MEMS sensors for heading drift and altitude error correction

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 270-281 ◽  
Author(s):  
Xiaochun Tian ◽  
Jiabin Chen ◽  
Yongqiang Han ◽  
Jianyu Shang ◽  
Nan Li
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Three Dimensional ◽  
Step Height ◽  
Reference Trajectory ◽  
Correction Algorithm ◽  
Content Type ◽  
Pedestrian Navigation ◽  
Smoothing Filter ◽  
Zero Velocity

Purpose This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space. Design/methodology/approach A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter. Findings The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent. Originality/value Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis

2020 ◽  
Vol 65 (6) ◽  
pp. 653-671 ◽  
Author(s):  
Nikiforos Okkalidis ◽  
Kenneth P. Camilleri ◽  
Alfred Gatt ◽  
Marvin K. Bugeja ◽  
Owen Falzon
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Parameters Estimation ◽  
Estimation Methods ◽  
Trajectory Estimation ◽  
Advantages And Disadvantages ◽  
Zero Velocity ◽  
Gait Parameters ◽  
Velocity Interval ◽  
Kinematic Gait Analysis

AbstractThe use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors’ utilization; (3) foot pose and trajectory estimation methods. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors’ utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.

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