Zero Velocity Update Research Articles

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.

A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.

Compensating the NLoS Occlusion Errors of UWB for Pedestrian Localization With MIMU

The Ultra-Wideband (UWB) technology has become a competitive choice for indoor pedestrian localization due to its advantages in accuracy, compact size, reliability, power efficiency, and easy setup. However, the None-Line-of-Sight (NLoS) occlusion caused by the human body parts and obstacles in its propagation path may introduce significant location errors that limit its performance and field of application. To handle the NLoS occlusion errors and promote the localization accuracy for complex indoor environments, a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (MIMU)-assisted UWB localization system with NLoS occlusion error compensation techniques is provided. Firstly, an NLoS occlusion detection method using the built-in channel information of the UWB transceivers and the variation pattern of anchor-node distance is proposed. Secondly, an Extended Kalman Filter (EKF)-based attitude computation and a Zero-Velocity Update (ZUPT)-based continuous gait segmentation are introduced for inertial navigation during occlusion intervals. Then, the UWB and IMU parameters are integrated with an Unscented Kalman Filter (UKF) framework to compensate for the occlusion error and improve the accuracy and robustness. Finally, a battery-powered miniature wearable device is developed and the proposed techniques are verified with experimental studies. The results demonstrate the feasibility and effectiveness of the presented techniques, which provides a good reference for related Internet of Things (IoT) applications.

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

Abstract. In recent years, most people use commercial integrated navigation systems to develop navigation algorithms. However, due to the different levels of sensors on the market, it is difficult to customize commercial systems and leads to limited development of navigation algorithms. Therefore, the purpose of this research is to develop a real-time integrated navigation system EGI-1000 (Embedded GNSS and INS) including software and hardware, and effectively reduce the cost with the commercial price. The real-time integrated navigation system EGI-1000 contains a navigation-grade IMU, IMU1000 and NovAtel OEM 7720 GNSS receiver module. In this research, the integration process can be divided into three parts. The first part is the integration of hardware, and the architecture diagram of the real-time integrated navigation system will be displayed. The second part is the pre-processing of data. In the multi-sensor time synchronization problem, this research will propose a method about cross-correlation to validate whether the timestamp of IMU data is delay. The last part is algorithm about fusing data from multiple sensors and motion constraints. Extended Kaman Filter (EKF) will be the core and motion constraints including Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) are integrated in the Loosely Coupled (LC) scheme. The calibration of Inertial navigation Measurement Unit (IMU) will also be conducted to determine the parameter in algorithm. The results of the experiments will be shown in this paper. Both of hardware and navigation algorithm in the integrated navigation system of this research are used to conduct multiple experiment including open sky environments, GNSS challenging environments, and GNSS denied environment. In comparison with the reference data, the navigation accuracy of the developed integrated navigation system can achieve centimeter-level accuracy (“Active Control” level and “Where in lane” level) in open sky and GNSS challenging environments. According to the propagation error theory, the result in GNSS denied environment also meet the expected value. The navigation algorithm is also feasible for the commercial integrated navigation system.

A yaw correction method for pedestrian positioning using two low-cost MIMUs

In pedestrian inertial navigation system, the traditional zero-velocity update (ZUPT) method establishes a velocity constraint. Based on the Kalman filter algorithm, the velocity error is utilized to correct the position drift. However, the yaw error cannot be observed. In this study, two low-cost magnetometers/inertial measurement units (MIMUs) are tied to the foot and shank, respectively. To improve the positioning accuracy, two error correction methods are developed. Firstly, the yaw angle coordinate is chosen as a Kalman filter measurement vector. Secondly, a geometric constraint is proposed. It includes degree of freedom constraint, angle constraint, and position vector constraint. Finally, different indoor and outdoor experiments are employed to verify the effectiveness of the proposed method. The average distance error percentage of the proposed method is less than 2%, and the heading bias is less than 5° within 5.05 min. Thus, the estimated paths are more in line with the actual trajectories.

An adaptive threshold method with error correction for pedestrian inertial navigation system

As an effective method for reducing accumulated errors in pedestrian navigation systems based on foot-mounted inertial measurement unit, the zero velocity update is widely used in global navigation satellite system signal rejection environments that require accurate zero velocity detection. However, the detection accuracy of the classical fixed threshold method is not satisfactory under complex gaits. In this paper, an adaptive threshold method with error correction is proposed to increase the detection accuracy without adding sensors. In this method, an adaptive threshold model based on gait intensity, which can dynamically adjust the zero velocity detection threshold under complex gaits, is established. Afterward, error correction is suggested to identify and correct misjudgments in zero velocity detection by analyzing the zero velocity interval in one gait cycle. We build an actual pedestrian inertial navigation system to evaluate the proposed method. The experimental results demonstrate that the average zero velocity detection accuracy of the proposed method under complex gaits of different people is 98.67%, and the end-to-end positioning error is about 0.53% of the total distance during long-distance movement.

Free-walking: Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system (INS), which is frequently used in emergency rescue operations and other situations, has the benefits of not relying on infrastructure, high positioning frequency, and strong real-time performance. However, the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time. This paper aims to enhance the accuracy of zero-velocity interval (ZVI) detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet. Aiming at the observational noise problem of low-cost inertial sensors, we utilize a denoising autoencoder to automatically eliminate the inherent noise. Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error, we propose a sample-level ZVI detection algorithm based on the U-Net neural network, which effectively solves the problem of mislabeling caused by sliding windows. Aiming at the problem that Zero-Velocity Update (ZUPT) cannot suppress heading and altitude error, we propose a bipedal INS method based on the equation constraint and ellipsoid constraint, which uses foot-to-foot distance as a new observation to correct heading and altitude error. We conduct extensive and well-designed experiments to evaluate the performance of the proposed method. The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

A tri-IMUs pedestrian positioning system based on single-lower-limb kinematic constraints

Single-sensor zero-velocity-update (ZUPT)-aided pedestrian inertial navigation system (PINS) is a conventional method. However, the single-sensor systematic error still causes imprecise estimation of step length and high drift of orientation. In this study, three magnetometers and inertial measurement units are mounted on single-leg toe, shank, and thigh, respectively. Gaits are divided into two phases: stance and swing. Based on the biomechanical characteristics of a human lower limb, a velocity-position estimation algorithm is proposed. The positions of the three sensors are fused by dynamic geometric relationships, which are formulated through the natural lower-limb model and attitude estimation. A velocity-difference constraint is proposed to suppress the divergence of velocity, and Coriolis-based velocity correction is designed to observe velocities of the toe, shank, and thigh at the stance phase. A MAHONY-linear-Kalman framework separately estimates attitudes, velocities, and positions to reduce calculations. The proposed method is compared with foot-mounted, shank-mounted, and thigh-mounted ZUPT-aided PINS as well as a dual-sensor foot-to-foot algorithm through experiments. In the flat terrain experiment, compared with the errors of the Shank-mounted INS, the Thigh-mounted INS, the Foot-mounted INS, the Foot-to-Foot Constraint Method, the root mean squared error (RMSE) of the proposed method can be reduced by 40.34%, 71.85%, 29.53%, and 7.08%, respectively. In the slope experiment, compared with the other four methods, the RMSE of the proposed method can be reduced by 80%, 86.74%, 67.54%, and 61.86%, respectively. In the natural terrain experiment, compared with the other four methods, the RMSE of the proposed method can be reduced by 81.63%, 96.27%, 70%, and 60.87% respectively. The results show that the proposed method greatly suppresses the positioning and orientation errors in different scenes.

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

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.

A Robust Foot-Mounted Positioning System Based on Dual IMU Data and Ultrasonic Ranging

Foot-mounted positioning system (FPS) is regarded as an important application for GPS-denied scenes, including but not limited to emergency rescue, medical health, and individual soldier positioning. With the assistance of zero-velocity update technology (ZUPT), the basic positioning function can be realized, while the largest problem is that the accumulation of systematic heading drift and positioning error cannot be completely constrained. This article presents a robust FPS based on dual inertial measurement unit data and ultrasonic ranging (FPS-DU). Bidirectional long short-term memory (Bi-LSTM) network is developed for the recognition of quasi-static (QS) period for the performance enhancement of ZUPT, and multilevel constraints are modeled for decreasing the cumulative error of the single foot-mounted model. In addition, a unified Kalman-based location estimator is developed combining dual foot-mounted inertial data and sphere constraint-based ultrasonic ranging results, enhanced by a robust ultrasonic outlier detector. Comprehensive experiments demonstrate that the proposed FPS-DU realizes much more accurate and robust positioning accuracy (between 0.371% and 0.548% under different test environments and traveled distances) compared with traditional single FPSs (S-FPSs).

A Novel Three-Dimensional Positioning Method for Foot-Mounted Pedestrian Navigation System Using Low-Cost Inertial Sensor

Aiming at the problem that position errors accumulate rapidly in foot-mounted pedestrian navigation systems using a low-cost inertial sensor, a novel three-dimensional (3D) positioning method is proposed. First, when the foot is still, an improved zero velocity detection method that assigns different weights to each inertial measurement unit (IMU) data in a sliding window is designed to reduce positioning errors. When the foot is in swing, a lateral velocity restriction method is proposed by analyzing the gait characteristics of pedestrians. In addition, when pedestrians need altitude positioning in the building, the stair step height is also applied to the zero-velocity update (ZUPT)-aided inertial navigation system, which can effectively improve altitude positioning accuracy. Experiments under multi-gait modes show that the proposed zero velocity detection method can achieve smaller positioning errors compared with other traditional zero velocity detection methods. Moreover, the trajectory estimated by the proposed method has a higher coincidence with the real trajectories, the two-dimensional (2D) plane positioning error is less than 0.9% and the average altitude positioning error is only 0.12 m.

Threshold-Free Phase Segmentation and Zero Velocity Detection for Gait Analysis Using Foot-Mounted Inertial Sensors

Gait analysis is a prosperous tool for the clinical evaluation and diagnosis. In this article, a portable gait analysis system based on foot-mounted inertial sensors is established. A threshold-free method using a long short-term memory recurrent neural network is constructed to segment four typical gait phases in a gait sequence for the temporal parameters analysis. Segmentation accuracy reaches over 95% across recruited subjects with distinct gait patterns, which is significantly superior when compared with traditional machine learning methods. The zero-velocity indicator is generated successively according to the segmented sequence to accomplish zero velocity update for the spatial parameter calculation. The accuracy of the proposed system is also validated through the OptiTrack in the lab. The comparison result of the stride length shows that the error between the two systems is less than 2%, which demonstrates that our system can satisfy the demand in the clinical.

A zero-velocity update method based on neural network and Kalman filter for vehicle-mounted inertial navigation system

Zero-velocity update (ZUPT) is an effective method of restraining the error divergence of the inertial navigation system (INS). The correct detection of zero-velocity points and an appropriate filtering algorithm are the key factors for the success of ZUPT. In this paper, a ZUPT method for vehicle-mounted INS based on a neural network (NN) and Kalman filter is proposed. The efficiency and accuracy of the zero-velocity detection is improved by the NN. The precision of the proposed method can reach 99.19%, and the recall rate is improved by 24% compared with the method based on the support vector machine. In addition, this method has similar accuracy and better real-time performance than the method based on a long short-term memory. Based on the zero-velocity detection by the NN, the navigation error is estimated and compensated by the Kalman filter. The effectiveness of the proposed method is proved by a vehicular experiment that shows that the velocity error is reduced to 24.2% and the position error is reduced to 9.5%.

Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

To achieve higher automation level of vehicles defined by the Society of Automotive Engineers, safety is a key requirement affecting navigation accuracy. We apply Light Detection and Ranging (LiDAR) as a main auxiliary sensor and propose LiDAR-based Simultaneously Localization and Mapping (SLAM) approach for Positioning, Navigation, and Timing. Furthermore, point cloud registration is handled with 3D Normal Distribution Transform (NDT) method. The initial guess of the LiDAR pose for LiDAR-based SLAM comes from two sources: one is the differential Global Navigation Satellite System (GNSS) solution; the other is Inertial Navigation System (INS) and GNSS integrated solution, generated with Extended Kalman Filter and motion constraints added, including Zero Velocity Update and Non-Holonomic Constraint. The experiment compares two initial guesses for scan matching in terms navigation accuracy. To emphasize the importance of a multi-sensor scheme in contrast to the conventional navigation method using the stand-alone system, the tests are conducted in both open sky area and GNSS signal block area, the latter might cause Multipath and Non-Line-Of-Sight effects. To enhance the navigation accuracy, the Fault Detection and Exclusion (FDE) mechanism is applied to correct the navigation outcome. The results show that the application of NDT and FDE for INS/GNSS integrated system can not only reach where-in-lane level navigation accuracy (0.5 m), but also enable constructing the dynamic map.

Virtual Beam Aided SINS/DVL Tightly Coupled Integration Method With Partial DVL Measurements

Strapdown inertial navigation system (SINS)/ Doppler velocity log (DVL) tightly coupled (TC) fusion is a popular scheme as it offers increased robustness compared to other approaches. In TC, DVL beam velocities are separately integrated with SINS in the navigation filter. To enhance the performance of SINS/DVL TC integration system in situations of partial DVL beam measurements, virtual beam aided solutions are proposed, theoretically derived, and experimentally evaluated. To that end, we analyze the geometrical relation between the DVL beam configuration and a zero velocity vector assumption and derive a zero velocity update (ZUPT) aided virtual beam method to continuously operate the TC fusion. Further, to avoid the precondition limitation of ZUPT method, a least squares support vector machine (LS-SVM) aided virtual beam construction algorithm together with its SINS/DVL TC integration scheme is proposed. Our experiments show the advantages and disadvantages of the ZUPT aided solution under different scenarios, and indicate the effectiveness of the LS-SVM aided virtual beam solution during short partial beam measurement periods.

A Practical Hybrid Height Estimation Algorithm Based on FM-Aided Motion Mode Recognition

Height estimation is critical for precise indoor localization in multistorey buildings. Traditionally, it is achieved by the barometric altimetry (BA)-based method, whose accuracy suffers from ambient barometric pressure fluctuations. Zero-velocity-update (ZUPT)-aided acceleration integration is another applicable approach, where zero velocity moments are introduced as calibration reference to limit error accumulation. However, zero velocity moments are derived from foot-mounted sensors in the stairs-climbing scenario, making it unavailable for other sensor locations and scenarios such as elevators. In this article, we propose a hybrid height estimation algorithm with both high accuracy and improved practicability. With the assistance of motion mode recognition (MMR), we apply a constraint on height estimations from both the BA-based method and the integration-based method, and then produce our height estimation by fusing the constraint heights. First, four typical motion modes comprising standing still, plane walking, climbing stairs, and taking elevator are recognized with features from acceleration, barometric pressure, and frequency-modulated (FM) signal. Second, we constrain the height estimations based on MMR results. To improve the BA-based method, a reference pressure update strategy is proposed to make the method applicable in an environment without reference barometers. Besides, the step-based integration strategy and the reverse compensation strategy are designed to optimize integration-based heights in stairs and elevators scenarios, respectively. Finally, the height estimations from these two sources are fused adaptively. Experiment results show that the accuracy of the proposed hybrid method outperforms that of the BA-based method. Moreover, the introduction of FM signal features effectively improves the performance of MMR and, thus, height estimation.

Evaluation of the Benefits of Zero Velocity Update in Decentralized Extended Kalman Filter-Based Cooperative Localization Algorithms for GNSS-Denied Multi-Robot Systems

Evaluation of the Benefits of Zero Velocity Update in Decentralized Extended Kalman Filter-Based Cooperative Localization Algorithms for GNSS-Denied Multi-Robot Systems

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques.

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

A Novel Adaptive Zero Velocity Detection Algorithm Based on Improved General Likelihood Ratio Test Detector

The zero-velocity update (ZUPT) method is an effective technique, which has been widely used in low-cost foot-mounted inertial navigation systems. It can mitigate the accumulated error by using the fact that, when the foot is stationary, the velocity should be zero. However, it is hard to detect the zero-velocity phases (ZVPs) correctly in various motion modes. To deal with this problem, a novel adaptive zero velocity detection algorithm based on an improved general likelihood ratio test (IGLRT) detector is proposed. The prior knowledge of the proposed IGLRT detector is the minima output value of inertial sensors, which is determined by an improved peak-valley detection method, while the prior knowledge of the traditional general likelihood ratio test (GLRT) method is based on the hypothesis that, when the foot is stationary, the magnitude of angular rates is zero, and the magnitude of accelerometers is equal to the magnitude of gravitation. Also, an adaptive method is proposed to enhance the robustness of the IGLRT, where the thresholds of IGLRT can be adjusted adaptively according to the amplitude of accelerometers. Field tests show that the proposed algorithm performs better than conventional and state-of-the-art approaches in mixed motion modes.

An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter

At present, the traditional indoor pedestrian navigation methods mainly include pedestrian dead reckoning (PDR) and zero velocity update (ZUPT), but these methods have the problem of error divergence during long time navigation. To solve this problem, under the condition of not relying on the active sensing information, combined with the characteristics of particles “not going through the wall” in the indoor map building structure, an improved adaptive particle filter (PF) based on the particle “not going through the wall” method is proposed for pedestrian navigation in this paper. This method can restrain the error divergence of the navigation system for a long time. Compared to the traditional pedestrian navigation method, based on the combination of indoor map assistance (MA) and particle filter, a global search method based on indoor MA is used to solve the indoor positioning problem under the condition of the unknown initial position and heading. In order to solve the problem of low operation efficiency caused by the large number of particles in PF, a calculation method of adaptively adjusting the number of particles in the process of particle resampling is proposed. The results of the simulation data and actual test data show that the proposed indoor integrated positioning method can effectively suppress the error divergence problem of the navigation system. Under the condition that the total distance is more than 415.44 m in the indoor environment of about 2600 m2, the average error and the maximum error of the position are less than two meters relative to the reference point.