Zero Velocity Update Research Articles

An integrated positioning method with IMU/UWB based on geometric constraints of foot-to-foot distances

Zero velocity update aided inertial navigation system (INS) and ultra-wideband (UWB) technology are widely used in pedestrian positioning. However, INS has significant cumulative error and the application environment of UWB positioning system is limited. In this study, an integrated positioning method with IMU/UWB based on geometric constraints of foot-to-foot distances is proposed. The gait of pedestrian is divided into three phases: stance, swing, and stride. Then, stance phase correction model, swing phase ranging model and stride phase size model are established, respectively. The magnetic induction intensity, accelerations, velocities, and the foot-to-foot distances measured by UWB are used to construct the constraints. Extended Kalman filter (EKF) and error state Kalman filter (ESKF) are utilized to estimate the attitudes and positions optimally. Several experiments in various environments are carried out to verify the proposed method. The results show that the proposed method has high accuracy and good robustness to the environments.

The Zero-Velocity Correction Method for Pipe Jacking Automatic Guidance System Based on Fiber Optic Gyroscope.

The pipe jacking guidance system based on a fiber optic gyroscope (FOG) has gained extensive attention due to its high degree of safety and autonomy. However, all inertial guidance systems have accumulative errors over time. The zero-velocity update (ZUPT) algorithm is an effective error compensation method, but accurately distinguishing between moving and stationary states in slow pipe jacking operations is a major challenge. To address this challenge, a "MV + ARE + SHOE" three-conditional zero-velocity detection (TCZVD) algorithm for the fiber optic gyroscope inertial navigation system (FOG-INS) is designed. Firstly, a Kalman filter model based on ZUPT is established. Secondly, the TCZVD algorithm, which combines the moving variance of acceleration (MV), angular rate energy (ARE), and stance hypothesis optimal estimation (SHOE), is proposed. Finally, experiments are conducted, and the results indicate that the proposed algorithm achieves a zero-velocity detection accuracy of 99.18% and can reduce positioning error to less than 2% of the total distance. Furthermore, the applicability of the proposed algorithm in the practical working environment is confirmed through on-site experiments. The results demonstrate that this method can effectively suppress the accumulated error of the inertial guidance system and improve the positioning accuracy of pipe jacking. It provides a robust and reliable solution for practical engineering challenges. Therefore, this study will contribute to the development of pipe jacking automatic guidance technology.

Assessment of lumbar disc herniation-impaired gait by using IMU data fusion method

The inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients’ subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH.

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array.

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

Assessing the validity of the zero-velocity update method for sprinting speeds.

The zero-velocity update (ZUPT) method has become a popular approach to estimate foot kinematics from foot worn inertial measurement units (IMUs) during walking and running. However, the accuracy of the ZUPT method for stride parameters at sprinting speeds remains unknown, specifically when using sensors with characteristics well suited for sprinting (i.e., high accelerometer and gyroscope ranges and sampling rates). Seventeen participants performed 80-meter track sprints while wearing a Blue Trident IMeasureU IMU. Two cameras, at 20 and 70 meters from the start, were used to validate the ZUPT method on a stride-by-stride and on a cumulative distance basis. In particular, the validity of the ZUPT method was assessed for: (1) estimating a single stride length attained near the end of an 80m sprint (i.e., stride at 70m); (2) estimating cumulative distance from ∼20 to ∼70 m; and (3) estimating total distance traveled for an 80-meter track sprint. Individual stride length errors at the 70-meter mark were within -6% to 3%, with a bias of -0.27%. Cumulative distance errors were within -4 to 2%, with biases ranging from -0.85 to -1.22%. The results of this study demonstrate the ZUPT method provides accurate estimates of stride length and cumulative distance traveled for sprinting speeds.

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 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.

Mechanization of a Robust Navigation Scheme for Land Applications Employing GNSS/INS Augmented with Zero Velocity Update

Mechanization of a Robust Navigation Scheme for Land Applications Employing GNSS/INS Augmented with Zero Velocity Update

Zero-Velocity Update-Based GNSS/IMU Tightly Coupled Algorithm with the Constraint of the Earth's Rotation Angular Velocity for Cableway Bracket Deformation Monitoring.

Cableways have been widely used in industrial areas, cities, and scenic spots due to their advantages, such as being a convenient mode of transportation, time-saving, labor-saving, and low cost, as well as offering environmental protection. To ensure the safe operation of a cableway, based on the characteristic that the velocity of the cableway bracket is approximately zero in a static deformation monitoring environment, a deformation monitoring method called zero velocity update (ZUPT)-based GNSS/IMU tightly coupled algorithm with the constraint of the Earth's rotation angular velocity was proposed. The proposed method can effectively solve the problem of a single GNSS being unable to output attitude, which is directly related to the status of wire ropes and cable cars. Meanwhile, ZUPT is used to restrain the Kalman filter's divergence when IMU is stationary. However, the improvements of ZUPT on attitude are not obvious, so the constraint of the Earth's rotation angular velocity was applied. The performance of the proposed method was evaluated through monitoring the cableway bracket of the Yimeng Mountain Tourism area in Shandong. Compared with the ZUPT-based GNSS/IMU tightly coupled algorithm (ZUPT-TC), the proposed method can further constrain the error accumulation of IMU while stationary and, therefore, it can provide reliable position and attitude information on cableway brackets.

THE DEVELOPMENT AND VALIDATION OF A TACTICAL GRADE EGI SYSTEM FOR LAND VEHICULAR NAVIGATION APPLICATIONS

Abstract. Over recent years, the utilization of commercially available integrated navigation systems for the development of navigation algorithms has become increasingly commonplace. Nevertheless, the wide range of sensor quality on the market complicates system customization and restricts the evolution of navigation algorithms. This study aims to address these issues by creating an affordable, tactical-grade, real-time integrated navigation system, EGI-500 (Embedded GNSS and INS), encompassing both hardware and software components. EGI-500 incorporates a tactical-grade IMU500 and a Septentrio Mosaic-X5 GNSS receiver module. The integration process is segmented into three distinct stages. The first involves hardware integration, with an illustrative architecture diagram of the real-time navigation system. Second, we focus on data preprocessing, where a cross-correlation approach is proposed to tackle multi-sensor time synchronization issues, specifically to determine potential time lags in IMU data. The final phase covers the fusion of multi-sensor data and motion constraints. The Extended Kalman Filter (EKF) forms the backbone of this part, with Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) being integrated into the Loosely Coupled (LC) scheme. Furthermore, the IMU calibration process is performed to ascertain necessary algorithmic parameters. Experimental results, conducted in diverse environments (open sky, GNSS challenging, and GNSS denied), will be presented in this paper. Comparisons with reference data indicate that the navigation accuracy of the developed integrated system, both in terms of hardware and navigation algorithm, achieves expected meter-level accuracy, fulfilling the "Which Lane" and "Which Road" level criteria in varied environments. Furthermore, outcomes from the GNSS denied environment align with predictions based on propagation error theory, demonstrating the feasibility of our navigation algorithm for tactical integrated navigation systems.

A lightweight odometry network for GNSS/INS integration during GNSS outages

In challenging environments like urban canyons and tunnels, the Global Navigation Satellite System (GNSS) signal can be interrupted. When this happens, the integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation system relies solely on INS, resulting in rapid dispersion of positioning accuracy over time. Incorporating odometer information into a filter algorithm is one potential solution for correcting INS errors and improving navigation accuracy. However, this approach increases the cost and power consumption of the system. To implement an odometer-assisted integrated navigation system without inflating cost and power consumption, we propose a lightweight odometer network, LONet. This network can emulate a wheeled odometer, determine the carrier’s forward velocity using Inertial Measurement Unit (IMU) output data, and utilize non-holonomic constraint (NHC) and zero velocity update (ZUPT) to maintain the system’s positioning accuracy. To evaluate the performance of our network, we conducted velocity estimate experiments across different velocity intervals. The results demonstrated that our network requires fewer parameters and produces lower errors in estimated velocity compared to state-of-the-art networks. Furthermore, we integrated LONet into the navigation system to mitigate the effects of GNSS signal outages. The results show that the LONet-assisted integrated navigation system achieved horizontal errors comparable to, and sometimes lower than, those obtained using a real wheeled odometer.

Pedestrian Inertial Positioning Method Based on Foot Quasi-Zero Velocity Observation Under Multiple Motion Modes

As the traditional zero velocity update (ZUPT) algorithm based on fixed threshold is difficult to solve the problem of effective positioning for pedestrians under multiple motion modes, a simpler, flexible and stable inertial positioning algorithm is proposed in this paper. A foot kinematic model based on rotating “L" shaped rigid body is proposed to describe the process from heel landing to heel off the ground. The quasi-zero velocity interval (QZVI) of foot motion is defined, and a method to determine the QZVI based on peak detection and gait cycle constraint is proposed to avoid the dependence of system on predetermined threshold. The proposed foot kinematic model is used to calculate the “near zero" velocity in the QZVI. Then difference between the calculated “near zero" velocity and the inertial navigation solution velocity is used as the observation of Kalman filter to estimate the position error, velocity error and attitude error of inertial navigation system (INS). Experiments show that the proposed algorithm has positioning error relative to the mileage of less than 2% under the mileage of 500m or less, effectively adapting to different motion modes such as fast walking, slow walking and running, so as to realize the accurate autonomous positioning under the condition of multiple motion modes.

A Novel Minimum Distance Constraint Method Enhanced Dual-Foot-Mounted Inertial Navigation System for Pedestrian Positioning

Foot-mounted inertial navigation system (Foot-INS) with the zero velocity update (ZUPT) has become one of the indispensable technical means in professional pedestrian positioning fields due to the advantages of self-constraint and immune to environmental factors. The dual-Foot-INS can provide more excellent autonomous positioning performance than a single-Foot-INS because it utilizes more opportunities for zero velocity correction and additional distance constraint information. However, the classical dual-Foot-INS does not fully exploit the distance constraint potential for positioning improvement. In this paper, we proposed a novel minimum distance constraint (MDC) method that achieves higher positioning accuracy than the traditional dual-Foot-INS methods. To obtain an accurate and consistent state estimation under the nonlinear distance constraint problem, we propose an iterative distance constraint (IDC) algorithm. The IDC is transformed into an approximate linear constraint model, and an alternative estimate is obtained by the estimation projection method. To solve the problem that the distance constraint moment in the traditional method is affected by the recursive foot positions, we propose a more reasonable and reliable minimum distance moment detection (MDMD) method. The proposed MDMD method maximizes the positioning performance improvement of the dual-foot pedestrian system. Two rigorous experimental tests with a long walking trajectory without turn-around and close-loop were conducted to verify the effectiveness of the proposed method, the positioning error of the proposed method is reduced by 83.5% and 62.9% compared to the classical ZUPT and minimum distance constraint methods, respectively.

Real-Time Speed Monitoring of Elevator System Based on Low-Cost IMU

Condition monitoring techniques are widely used in various industries to increase efficiency and reduce maintenance costs, but it has yet to be extensively researched in elevator system. In this paper, we propose a model-based method with high accuracy and strong robustness for real-time speed monitoring in elevator system using low-cost inertial measurement unit (IMU) as the data acquisition device. The algorithm uses attitude correction, Kalman filter within overlapping sliding window, and zero-velocity update to eliminate the interference, including the low precision and random placement angle of IMU, different motion characteristics of the elevator, and slight vibration caused by the external environment. The experimental results indicate that the performance of the algorithm on the test sets can be comparable to that of classical supervised learning models and outperform the direct integration method. Subsequent experiments have verified that the proposed method can maintain high accuracy for a long time in real scenarios.

Walking Trajectory Estimation Using Multi-Sensor Fusion and a Probabilistic Step Model.

This paper presents a framework for accurately and efficiently estimating a walking human's trajectory using a computationally inexpensive non-Gaussian recursive Bayesian estimator. The proposed framework fuses global and inertial measurements with predictions from a kinematically driven step model to provide robustness in localization. A maximum a posteriori-type filter is trained on typical human kinematic parameters and updated based on live measurements. Local step size estimates are generated from inertial measurement units using the zero-velocity update (ZUPT) algorithm, while global measurements come from a wearable GPS. After each fusion event, a gradient ascent optimizer efficiently locates the highest likelihood of the individual's location which then triggers the next estimator iteration.The proposed estimator was compared to a state-of-the-art particle filter in several Monte Carlo simulation scenarios, and the original framework was found to be comparable in accuracy and more efficient at higher resolutions. It is anticipated that the methods proposed in this work could be more useful in general real-time estimation (beyond just personal navigation) than the traditional particle filter, especially if the state is many-dimensional. Applications of this research include but are not limited to: in natura biomechanics measurement, human safety in manual fieldwork environments, and human/robot teaming.

A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments.

This paper proposes a novel Blockchain-based indoor navigation system that combines a foot-mounted dual-inertial measurement unit (IMU) setup and a zero-velocity update (ZUPT) algorithm for secure and accurate indoor navigation in GNSS-denied environments. The system estimates the user's position and orientation by fusing the data from two IMUs using an extended Kalman filter (EKF). The ZUPT algorithm is employed to detect and correct the error introduced by sensor drift during zero-velocity intervals, thus enhancing the accuracy of the position estimate. The proposed Low SWaP-C blockchain-based decentralized architecture ensures the security and trustworthiness of the system by providing an immutable and distributed ledger to store and verify the sensor data and navigation solutions. The proposed system is suitable for various indoor navigation applications, including autonomous vehicles, robots, and human tracking. The experimental results provide clear and compelling evidence of the effectiveness of the proposed system in ensuring the integrity, privacy, and security of navigation data through the utilization of blockchain technology. The system exhibits an impressive ability to process more than 680 transactions per second within the Hyperledger-Fabric framework. Furthermore, it demonstrates exceptional accuracy and robustness, with a mean RMSE error of 1.2 m and a peak RMSE of 3.2 during a 20 min test. By eliminating the reliance on external signals or infrastructure, the system offers an innovative, practical, and secure solution for indoor navigation in environments where GNSS signals are unavailable.

Prior Kinematic Information Fusion for Pedestrian Localization With Toe-Heel-Shank MIMUs

With the development of the micro-electronic mechanical system (MEMS) inertial measurement units (IMU), the foot-mounted pedestrian inertial navigation system (PINS) based on the zero velocity update (ZUPT) is widely used. The systematic errors of MEMS IMU cause low precision of PINS at swing phase. However, the bio-mechanical constraints, which can be used to suppress the divergence of PINS, exist in lower limbs. In this study, three magnetometer and IMUs are fixed on the toe, heel and shank, respectively. The attitude of each segment is estimated by a MAHONY filter. The gait is defined as four successive phases. For each phase, the velocity and position constraints are constructed based on dynamic geometric constraint and Coriolis theory. Based on the perfect measurement method, the kinematic constraints, which are calculated by inertial data and attitude, are added into the observed equations. The noises are considered as additive Gaussian white noise. The linear Kalman filter is set to iteratively estimate the optimal positioning result. Several experiments with different walking velocities and trajectories are performed. The results show that the algorithm which is proposed in this study, greatly suppresses the velocity and positioning error.

Adaptive Threshold-Based ZUPT for Single IMU-Enabled Wearable Pedestrian Localization

Without dependence on external anchors, the Micro-Electro-Mechanical System Inertia Measurement Unit (MIMU) allowing autonomous localization has promised great potential in wearable IoT applications, including kinematic analysis in sports, medical treatment, elderly care, and disaster rescue. However, the miniaturization of devices for unobtrusive sensing, algorithms minimizing the inherent accumulative errors of inertial devices, and the adaptivity of algorithms for various motion modalities are the key challenges. The removal of accumulative error in the continuous gait cycles with adaptive algorithms is a critical issue regarding localization accuracy, especially for low-cost devices. This investigation proposes an adaptive threshold-based Zero-velocity Update (ZUPT) algorithm to separate the timing of gait cycle phases and compensate for the residual velocity with a linear fitting approximation. The key contributions include: (1) a lightweight threshold-based zero-velocity detection algorithm to split the gait cycle phases of continuous walking; (2) a quaternion-based Extended Kalman Filter (EKF) algorithm to reduce the errors of the non-linear operations for attitude prediction; (3) a linear fitting method for compensating the residual velocity in each gait cycle of continuous walking; (4) the design of a miniature single-MIMU based foot-mountable wearable device and the corresponding experimental studies to verify the proposed methods. Results show that the relative error is less than 3.0% for 2D and 3D trajectories, and the tests with different locomotion patterns demonstrate the adaptivity of the proposed algorithm compared to its peers. The results show that the presented techniques are capable of handling accumulative errors for low-cost MIMU-based systems with good adaptivity.

Visual–Inertial Navigation System Based on Virtual Inertial Sensors

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments.