Zero Velocity Update Research Articles

IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping

With the development of indoor location-based services (ILBS), the dual foot-mounted inertial navigation system (DF-INS) has been extensively used in many fields involving monitoring and direction-finding. It is a widespread ILBS implementation with considerable application potential in various areas such as firefighting and home care. However, the existing DF-INS is limited by a high inaccuracy rate due to the highly dynamic and non-stable stride length thresholds. The system also provides less clear and significant information visualization of a person’s position and the surrounding map. This study proposes a novel wearable-foot IOAM-inertial odometry and mapping to address the aforementioned issues. First, the person’s gait analysis is computed using the zero-velocity update (ZUPT) method with data fusion from ultrasound sensors placed on the inner side of the shoes. This study introduces a dynamic minimum centroid distance (MCD) algorithm to improve the existing extended Kalman filter (EKF) by limiting the stride length to a minimum range, significantly reducing the bias in data fusion. Then, a dual trajectory fusion (DTF) method is proposed to combine the left- and right-foot trajectories into a single center body of mass (CBoM) trajectory using ZUPT clustering and fusion weight computation. Next, ultrasound-type mapping is introduced to reconstruct the surrounding occupancy grid map (S-OGM) using the sphere projection method. The CBoM trajectory and S-OGM results were simultaneously visualized to provide comprehensive localization and mapping information. The results indicate a significant improvement with a lower root mean square error (RMSE = 1.2 m) than the existing methods.

단축 가속도계의 Pseudo 바이어스 모델을 이용한 필터 기반 차량 속력 추정 알고리즘

This study presents a filter-based vehicle longitudinal speed estimation algorithm with improved accuracy by estimating the accelerometer bias using only a single-axis accelerometer in the section where the odometer reliability is low. Most vehicles use odometers to obtain speed information because odometers can measure speed with high accuracy under normal driving conditions. However, since the typical odometer has a characteristic that depends on the measured revolutions per minute of the vehicle wheel, if a slip situation occurs in which the wheel slides owing to a sudden start and sudden stop, then the odometer outputs an inaccurate speed measurement. To solve this problem, we redefine the sensor error model of the accelerometer attached to the vehicle and propose a method to estimate the attitude and self-bias component, which are error components that have large effects on the accelerometer, through an adaptive lowpass filter. In addition, we propose an approach to apply the zero-velocity update method to improve the accuracy of the estimated speed information by integrating the error-compensated acceleration value based on the filter. To evaluate the performance of the proposed algorithm, an actual vehicle experiment was performed, and the speed estimation results of the odometer disabled area were analyzed. The results confirmed that the accuracy of speed estimation was greatly improved.

Online calibrated, energy-aware and heading corrected pedestrian navigation with foot-mounted MARG sensors

The objective of this paper is to propose an energy-aware and gait corrected pedestrian dead reckoning (PDR) approach using foot-mounted magnetic, angular rate and gravity (MARG) sensors. Compared with existing algorithms of PDR, the proposed method aims to solve three main problems for real pedestrian applications. First, to avoid limitations of off-line calibration for personal step length parameters, we utilize the zero-velocity-update (ZUPT) aided pedestrian MARG performance to continuously compute one’s pose information. Meanwhile, it accumulates the moving distance for further estimation of one’s step length during the initialization process. Secondly, due to different pedestrian gaits implicating the heading deviation angle between one’s moving direction and heading, there are non-negligible impacts on pedestrian dead reckoning accuracy. The linear Kalman filter (KF) is used to recursively estimate the deviated heading during aforementioned initialization process. The third problem is related to the energy consumption. Following three aspects of adaptive energy saving work are devised: (i) energy-aware strategy for gyroscopes measurements acquisition is adopted to guarantee lower energy consumption. (ii) Modes switching mechanism of navigation computation is applied to the initialization and dead reckoning processes. (iii) De-sampling after initialization process has been invoked. Finally, real-world experiments are carried out to evaluate the performances of developed PDR system. The results show the efficiency of the suggested approach.

An Attitude Improvement Method of FOG-Based Measurement-While-Drilling Utilizing Backtracking Navigation Algorithm

Directional drilling utilizes the measurement-while-drilling (MWD) to provide attitude for drilling bit. A traditional MWD system, consisting of magnetometers and accelerometers, relies on expensive and huge nonmagnetic drill collar (NMDC) to shield from magnetic interference to obtain accurate attitude angles. A fiber optic gyroscope-based MWD (FOG-based MWD) that is based on inertial navigation is an alternative survey scheme. But, random vibration environment of underground introduces high-amplitude random noise to the FOGs. Besides, the auxiliary measuring equipment [such as global position system (GPS)] will not be available in underground environments, which will bring massive attitude error under pure inertial navigation. Therefore, combined with the engineering application conditions, this article proposes a modified attitude precision improvement approach of the FOG-based MWD. First, an autoregressive moving average (ARMA) error model for FOG is established to restrain the random vibration environment interference of underground. Second, combined with the FOG error model, a continuous backtracking navigation error model is developed to improve the azimuth accuracy. Moreover, the continuous backtracking algorithm frame is designed to estimate the attitude. Finally, a semi-physical experiment and a drilling test verify that the error model has an obvious effect on reducing the amplitude of FOG random noise. The proposed backtracking algorithm make the azimuth angle achieve a high-level estimation in short time. The result of the drilling test shows that the azimuth has an improvement of 48.79%, as compared with the traditional zero velocity update (ZUPT).

Pitch angle sliding variance test method based on Mahony filter for zero-velocity detection

The threshold judgment method is a comparatively simple and classic zero-velocity detection (ZVDT) method for zero-velocity update. To solve the problem with the existing threshold judgment method, where the ZVDT effect is critically diminished by sensor noise, motion state changes, and other factors, this paper proposes a pitch angle sliding variance test (PSVT) method based on Mahony filtering. First, a Mahony filtering algorithm is designed to merge and de-noise the original foot acceleration and angular velocity information to provide high-precision pitch angle information. Subsequently, based on the significant periodicity, isolation, and characteristics of foot pitch angle variation during pedestrian movement, a PSVT method is proposed for pedestrian ZVDT. Experimental results show that for the motions of different pedestrians in different scenes, the average single-step misjudgment rate in the zero-velocity interval of the proposed method is less than 10% overall. When compared with the generalized likelihood ratio test, the ZVDT effect is improved by approximately 30%. The method effectively improves the applicability and accuracy of ZVDT for pedestrian movement, and will be significant in improving the applicability, navigational accuracy, and practical use of pedestrian autonomous navigation systems.

A Pedestrian 3-D Position Estimation Method Based on Equality Constraint and Moving Platforms Correction Model

Inertial pedestrian navigation system (IPNS) has been broadly implemented for personnel navigation and positioning tasks without the preinstallation of infrastructure. However, the conventional foot-mounted IPNS that generally relies only on zero-velocity update (ZUPT) has serious heading drift and incorrectly judges foot as still phases when pedestrians take moving platforms (MPs; such as elevators and escalators). In this article, with the aid of six low-cost inertial sensors, a bipedal equality constraint method based on the Denavit–Hartenberg (DH) model is constructed to reduce heading drift. Then, an MP correction model that fuses detection, motion substates classification, and corresponding velocity constraint of MPs is proposed to achieve precise position tracking during elevator or escalator rides. Based on the equality constraint, the heading of the system is more accurate, which gives better play to the performance of the MP correction model. The robustness and effectiveness of the proposed algorithm are verified by multiple experiments in an office building, the positioning error in the rectangular 2-D experiment is less than 0.3%, and the horizontal positioning error and height positioning error in the comprehensive 3-D experiment are both less than 0.8%.

A Wearable Gait Analysis and Recognition Method for Parkinson's Disease Based on Error State Kalman Filter.

For the purpose of quantitative analysis, this paper proposes a wearable gait analysis method for Parkinson's disease (PD) to evaluates the motor ability. The error state Kalman filter (ESKF) is used for attitude estimation, and the gait parameters are modified by phase segmentation and zero velocity update (ZUPT) algorithm. In addition, this study uses gait parameters as classifier features to recognize abnormal gait, and compares the recognition effect with statistical features. The effect of our gait system is verified by comparison with the OptiTrack system, and the mean absolute error (MAE) of step length and foot clearance are 2.52 ±3.61 cm and 0.96 ±1.24 cm respectively. Forty Parkinson's patients and forty age-matched healthy people are recruited for gait comparison, the analysis results showed significant differences between the two groups. The abnormal gait recognition results show that gait features have stronger generalization ability than statistical features in leave-one-subject-out (LOSO) validation. The method proposed in this study can be applied to the gait analysis and objective evaluation of PD.

PINDOC: Pedestrian Indoor Navigation System Integrating Deterministic, Opportunistic, and Cooperative Functionalities

In this paper, we present a Pedestrian Indoor Navigation system integrating Deterministic, Opportunistic, and Cooperative functionalities (PINDOC) for multi-agent navigation. The deterministic module produces a navigation solution for each agent by utilizing a Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation System (INS) augmented with an altimeter and foot-to-foot range measurements. The opportunistic module augments the deterministic solutions with pseudorange measurements extracted from cellular Long-Term Evolution (LTE) signals. The navigation accuracy of each individual agent is further enhanced by cooperative localization using Ultra-WideBand (UWB)-based inter-agent range measurements. We developed a dedicated multi-sensor pedestrian navigation hardware testbed and conducted an experiment to evaluate the navigation performance of different combinations of components comprising PINDOC: ZUPT-aided INS, altimeter, foot-to-foot ranging, LTE signals, and inter-agent ranging. The experiment involved three agents, with one agent traversing a trajectory of 600 meters in 14 minutes, during which, the other two agents remained stationary. The traversed trajectory included terrains of flat surfaces, stairs, ramps, and elevators. The PINDOC system showed high accuracy, achieving a position Root-Mean-Squared Error (RMSE), maximum error, and final error of 0.93 m, 2.23 m, and 1.28 m over the 600-meter trajectory.

A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian.

The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects.

A Novel Suppression Method of Height Drift for Pedestrian Navigation With a Circular Hypotheses on Terrain Slope

The inertial navigation system (INS) based on zero velocity update (ZUPT) is a traditional method for pedestrian positioning. The height information of INS will drift over time. A common solution is to use external height information, such as barometer output, to compensate the drift. However, this method is easily affected by air temperature, wind speed, etc. A new geometric model method is proposed in this study to suppress the height drift. The inertial sensor is mounted at the shoe. The natural terrain with continuous slope changing is assumed to be circular arc-shaped every single step. A geometric model of three-dimensional step length is established using the horizontal step length and the pitch angle of the shoe. Four experiments were designed on flat, sloped, and natural terrains, respectively. The experimental results show that the accuracy of the proposed method is improved by more than 90% and 70% compared to the classical INS method and the INS method incorporating a barometer, respectively. The proposed method needs no external signal and is not influenced by the external environment. Furthermore, lightweight calculations make it appropriate for real-time applications.

Pedestrian Positioning Based on Dual Inertial Sensors and Foot Geometric Constraints

Foot-mounted inertial navigation with zero velocity update (ZUPT) is commonly used in pedestrian positioning. To suppress the accumulating position error owing to the low-cost inertial sensor, classical dual-foot pedestrian navigation method provides the compensation using the two feet's relative step length, whose values can be set an upper bound constraint or measured by ranging sensors. However, it suffers from low improvement and poor wearability. In this study, the two sensors are distributed on the toe and ankle of the same leg. The sensor-to-sensor position is unchangeable with the lower limb segments. This makes the uncoupled two subsystems be related. Through gait analysis, the step is divided into the stance and swing phases. For each phase, positions, velocities, and the related characteristics of the toe and the ankle are analyzed and modeled, respectively. A Kalman filter is constructed to fuse the position and velocity observations according to the geometric constraints and the foot vector. The Mahony filter by fusing the inertial sensor and magnetometer is adopted for the attitude improvement. Several experiments at low and high walking velocities on the plane and upstairs are carried out to verify the proposed method. The results show that compared with the conventional single-inertial measurement unit (IMU) ZUPT and dual-IMU two-foot constraint methods, the position and heading errors have been well suppressed.

Heading constraint algorithm for foot-mounted PNS using low-cost IMU

Foot-mounted pedestrian navigation system (PNS) is a common solution to pedestrian navigation using micro-electro mechanical system (MEMS) inertial sensors. The inherent problems of inertial navigation system (INS) by the traditional algorithm, such as the accumulated errors and the lack of observation of heading and altitude information, have become obstacles to the application and development of the PNS. In this paper, we introduce a heuristic heading constraint method. First of all, according to the movement characteristics of human gait, we use the generalized likelihood ratio test (GLRT) detector and introduce a time threshold to classify the human gait, so that we can effectively identify the stationary state of the foot. In addition, based on zero velocity update (ZUPT) and zero angular rate update (ZARU), the cumulative error of the inertial measurement unit (IMU) is limited and corrected, and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian. After simulation and experiments with low-cost IMU, the method is proved to reduce the localization error of end-point to less than 1% of the total distance, and it has great value in application.

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

<p>With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively. </p> <p> </p>

Real-Time Human Motion Capture Based on Wearable Inertial Sensor Networks

Wearable inertial motion capture, a new type of motion capture technology, mainly estimates the human posture in 3-D space through multisensor data fusion. The available method for sensor fusion is usually aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of a complex magnetic field environment. In this article, an extended Kalman filter (EKF) data fusion method is proposed to fuse the 9-axis sensor data. Meanwhile, the heuristic drift reduction (HDR) method is used to calibrate the accumulated error of a heading angle. In addition, the position in 3-D space is estimated by the foot-mounted zero-velocity-update (ZUPT) technique. Combining 3-D attitude and position, a biomechanical model of the human body is established to track the motion of a real human body. The EKF algorithm and position estimation methods are benchmarked against the golden standard, optical motion capture system, for various indoor experiments. In addition, various outdoor experiments are also conducted to verify the reliability of the proposed method. The results show that the proposed algorithm outperforms the available attitude estimation model in motion tracking and is feasible for 3-D human motion capture.

IMPROVEMENT OF LiDAR-SLAM-BASED 3D NDT LOCALIZATION USING FAULT DETECTION AND EXCLUSION ALGORITHM

Abstract. To meet the autopilot demand of autonomous vehicle, higher automation level accompanies with higher consideration of safety factor to improve navigation accuracy. Moreover, it shall be stable under diverse environment, e.g., semi-open sky, urban, traffic jam, etc, where conventional navigation methods, the Inertial Measurement Unit (IMU) and global Navigation Satellite System (GNSS), might be limited. Thus, auxiliary sensor, the light detection and ranging (LiDAR), is applied to provide additional information to assist navigation under GNSS challenging environment, and fulfil Simultaneous Localization and Mapping (SLAM). To initially align the LiDAR point cloud, initial pose is generated by Extended Kalman Filter (EKF) through Loosely Coupled (LC) scheme, assisting with motion constraints, including Zero Velocity Update (ZUPT), Non-Holonomic Constraints (NHC), and Zero Integrated Heading Rate (ZIHR) function. With point cloud after initial alignment, registration method applied in this research is point to distribution based-Normal Distribution Transform (P2D-NDT), with scan to dynamic map matching. However, pure LiDAR-SLAM estimated solution remains faults in each measurement, which will propagate through computation and leads to false navigation outcome. Therefore, this paper proposed Fault Detection, Isolation, and Exclusion (FDIE) scheme to exclude the faults in each step of LiDAR-SLAM process. The final estimated solution is compared to robust reference data, the results turn out that convention navigation method work well under stable GNSS signal environment, while significant accuracy enhancement is achieved with NDT and FDE under large initial pose offset, such as GNSS signal blocked area.

Stance Phase Detection for ZUPT-Aided INS Using Knee-Mounted IMU in Crawling Scenarios

This letter presents a study on the navigational accuracy of crawling using heel and knee-mounted inertial measurement units (IMU). The navigation method is based on a stance phase detection in combination with the zero-velocity UPdaTe algorithm and strapdown inertial navigation systems (INS). A parameter search considering window size, detector type, and statistical threshold was performed to determine the parameters that minimized the circular error probable (CEP). Under the experimental conditions, the knee-mounted IMU position rendered improved navigation accuracy compared with the heel-mounted IMU. Based on the IMU used in this experiment, accelerometer-based zero-velocity detectors appeared to have improved performance for the knee-mounted IMU compared with the angular rate energy and stance phase hypothesis detectors. Under the optimal parameters, the CEP/corresponding root-mean-square error (RMSE) for heel-mounted and knee-mounted IMUs was 0.95/1.05 m and 0.22/0.43 m, respectively, for ten crawling trials over a 42.6 m straight trajectory marking a 77.3% CEP and 59.1% RMSE improvement when using a knee-mounted versus a heel-mounted IMU.

Pedestrian Dead Reckoning with Low-Cost Foot-Mounted IMU Sensor.

In this paper, we researched Pedestrian Dead Reckoning (PDR) with one foot-mounted IMU sensor. The issues of PDR are magnetism noise and accumulated error due to the noise included in acceleration and gyro data. Two methods are proposed in this paper. First is the gait-phase-estimation method with pitch angle for the Zero Velocity Update algorithm. Second is a method for avoiding accumulated errors by updating the roll and pitch angles with acceleration. The two experiments were conducted to examine the error of gait-phase estimation and distance estimations. The relative error of distance was about 7.40% in the case of walking straight and about 12.27% in the case of a shifting travel direction.

New Zero-Velocity Detector for Low-Cost Inertial Pedestrian Navigation

A new Zero Velocity Interval (ZVI) detector is proposed and investigated in this article to augment the computation of a pedestrian’s position. In a low-cost pedestrian navigation system, the position of a pedestrian can be computed by using inertial navigation algorithms. The low-cost pedestrian navigation system employs an inertial measurement unit (IMU) comprised of accelerometer and gyroscope sensors to record acceleration and attitude rate. These measurements, when integrated mathematically, yield velocity and position. Similarly, attitude rate changes to attitude. The algorithm will then be able to figure out the changes in pedestrian position with the proper attitude. However, due to its low-cost nature, these sensors are built in such a way that their measurements are easily corrupted by noise, and once integrated mathematically, the measurement error grows exponentially. Zero Velocity Update (ZUPT) algorithm is frequently used to limit these errors. It works by detecting the ZVI that occur when the foot is on the ground. Assuming that the foot on the ground should have zero velocity, any remaining velocity measurements detected during this period are considered an error and are fed back to the navigation algorithm for correction. To detect the ZVI, a few commonly used detectors, namely Angular Rate Energy (ARE), Acceleration Moving Variance (MV), Acceleration Magnitude (MAG), and Generalized Likehood Ratio Test (GLRT) were revisited. These detectors were tested with real-world datasets of walking pedestrians. Then, a new detector, the Angular Rate Moving Variance (ARMV) detector, is proposed, and its performance is compared to that of the existing detectors.

A Pedestrian Indoor Navigation System Using Deep-Learning-Aided Cellular Signals and ZUPT-Aided Foot-Mounted IMUs

A signal-sensor-based indoor pedestrian navigation system is developed. The proposed system: 1) utilizes a foot-mounted inertial navigation system (INS), in which the accumulated errors are mitigated via a zero velocity update (ZUPT) approach; 2) exploits opportunistically cellular long-term evolution (LTE) signals in a deep neural network (DNN)-based synthetic aperture navigation (SAN) framework, in which the pedestrian’s motion is utilized to suppress multipath-induced errors. The proposed DNN-SAN-LTE-ZUPT-INS (DUALS) indoor pedestrian navigation system utilizes the complementary the desirable characteristics of both subsystems, coupled via two architectures: (a) loosely-coupled and (b) tightly-coupled. This paper designs and assesses both architectures experimentally in an indoor environment. The experimental study demonstrates a pedestrian traversing a trajectory of 600 m in 14 minutes, including a stationary period, straight segments, up/down the stairs, and riding in an elevator, while receiving signals from 4 LTE base stations (also known as evolved node Bs (eNodeBs)). The proposed tightly-coupled DUALS system exhibited a three-dimensional (3-D) position root mean-squared error (RMSE) of 1.34 m, outperforming the loosely-coupled DUALS, ZUPT-aided INS, and LTE-DNN-SAN, which achieved a position RMSE of 1.38, 1.49, and 1.97 m, respectively.

Symmetrical-Net: Adaptive Zero Velocity Detection for ZUPT-Aided Pedestrian Navigation System

Inertial navigation system (INS) is a practical method for indoor pedestrian navigation without pre-installation of infrastructure. Based on the fundamentals of human bipedal motion, zero velocity update (ZUPT) is a pervasive approach to tackle the accumulated error of inertial measurement units (IMU). While zero velocity detection plays a vital role in the algorithm, existing fixed-threshold methods to pick these pseudo-measurements of error-state Kalman Filter (ESKF) have the doubtful capability to fit various individuals in different motions. To address this issue, we propose the Symmetrical-Net leveraging deep Recurrent Convolutional Neural Networks (RCNNs) to detect the zero velocity interval adaptively. Additionally, two RCNNs are constructed in the symmetrical framework considering bidirectional IMU readings, which further improves the performance of the model. A comprehensive evaluation containing 87 different trajectories from 27 individuals has been conducted. The results show that the detection accuracy is up to 99.5% and 96.5% on the training and validation sets, respectively. It is verified that the precise and robust Symmetrical-Net can be a viable approach for the ZUPT-aided INS system.