Foot-mounted Pedestrian Navigation Research Articles

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.

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.

A single foot-mounted pedestrian navigation algorithm based on the maximum gait displacement constraint in three-dimensional space

Inertial navigation technology composed of inertial sensors is widely used in foot-mounted pedestrian positioning. However, inertial sensors are susceptible to noise, which affects the performance of the system. Zero-velocity update (ZUPT) is a traditional method utilized to suppress the cumulative error. Unfortunately, the walking distance calculated by a Kalman filter still has positional error. To improve the positional accuracy, in this work we propose a nonlinear Kalman filter with a spatial distance inequality constraint for a single foot. Since the stride distance between adjacent stance phases has an upper bound in plane and height, an inertial navigation system established by one inertial measurement unit is adopted to constrain the stride process. Eventually, the performance of the proposed method is verified by experiments. Compared with the single foot-mounted ZUPT method, the proposed method suppresses the plane error and the height error by 46.04% and 65.48%, respectively. For the dual-foot constraint method, the proposed constraint method can reduce the number of sensors while ensuring positioning accuracy. Moreover, the height error is reduced by 59.98% on average by optimizing the constraint algorithm. The experimental results show that the trajectory estimated by the proposed method is closer to the actual path.

A Robust GNSS/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians

Aiming at the problem of high-precision positioning of mass-pedestrians with low-cost sensors, a robust single-antenna Global Navigation Satellite System (GNSS)/Pedestrian Dead Reckoning (PDR) integration scheme is proposed with Gate Recurrent Unit (GRU)-based zero-velocity detector. Based on the foot-mounted pedestrian navigation system, the error state extended Kalman filter (EKF) framework is used to fuse GNSS position, zero-velocity state, barometer elevation, and other information. The main algorithms include improved carrier phase smoothing pseudo-range GNSS single-point positioning, GRU-based zero-velocity detection, and adaptive fusion algorithm of GNSS and PDR. Finally, the scheme was tested. The root mean square error (RMSE) of the horizontal error in the open and complex environments is lower than 1 m and 1.5 m respectively. In the indoor elevation experiment where the elevation difference of upstairs and downstairs exceeds 25 m, the elevation error is lower than 1 m. This result can provide technical reference for the accurate and continuous acquisition of public pedestrian location information.

A Novel Calibration Method for Gyro-Accelerometer Asynchronous Time in Foot-Mounted Pedestrian Navigation System.

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.

The Improved Constraint Methods for Foot-Mounted Pedestrian Three-Dimensional Inertial Navigation

The foot-mounted pedestrian navigation system (PNS) that uses microelectromechanical systems (MEMS) inertial measurement units (IMUs) to track the person’s position. However errors accumulate over time during inertial navigation solutions, which affects the positioning precision. In this paper, a multicondition zero velocity detector is used to detect the stance phase of gait. Then the errors are corrected in the stance phase and the swing phase, respectively, through the Kalman filter. When pedestrians are going up and down the stairs, the divergence of height will reduce the accuracy of three-dimensional positioning. In this paper, an accelerometer and a barometer are used to obtain altitude variation, and after that the stair condition detection (SCD) algorithm is proposed to correct the height of Kalman filter output and detect the walking state of pedestrians. Through theoretical research and field experiments, these algorithms are studied carefully. The results of the experiment show that the algorithm proposed in this paper can effectively eliminate errors and achieve more accurate positioning.

Adaptive Cardinal Heading Aided for Low Cost Foot-Mounted Inertial Pedestrian Navigation

The use of a low-cost MEMS-based Inertial Measurement Unit (IMU) provides a cost-effective approach for navigation purposes. Foot-mounted IMU is a popular option for indoor inertial pedestrian navigation, as a small and light MEMS-based inertial sensor can be tied to a pedestrian's foot or shoe. Without relying on GNSS or other external sensors to enhance navigation, the foot-mounted pedestrian navigation system can autonomously navigate, relying solely on the IMU. This is typically performed with the standard strapdown navigation algorithm in a Kalman filter, where Zero Velocity Updates (ZVU) are used together to restrict the error growth of the low-cost inertial sensors. ZVU is applied every time the user takes a step since there exists a zero velocity condition during stance phase. While velocity and correlated attitude errors can be estimated correctly using ZVUs, heading error is not because it is unobservable. In this paper, weextend our previous work to correct the heading error by aiding it using Multiple Polygon Areas (MPA) with adaptive weighting factor. We termed the approach as Adaptive Cardinal Heading Aided Inertial Navigation (A-CHAIN). We formulated an adaptive weighting factor applied to measurement noise to enhance measurement confidence. We then incorporated MPA heading into the algorithm, whereas multiple buildings with the same orientation are grouped together and assigned a specific heading information as a priori. Results shown that against the original CHAIN, the proposed Adaptive-CHAIN improved the position accuracy by more than five-fold.

Foot-Mounted Pedestrian Navigation Method by Comparing ADR and Modified ZUPT Based on MEMS IMU Array.

Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that combines adaptive deck reckoning (ADR) with the modified zero velocity update (ZUPT). In the proposed ADR algorithm, the IMUs with large drift errors on the array are selected and removed according to the step length and the track angle computed by each IMU on the array. Then, by using the step length and the track angle of each step computed by remaining IMUs, the foot position extracted from the traditional ZUPT model is estimated on the basis of least squares (LS) so as to improve the traveled distance calculation accuracy. Compared with the traditional IMU array fusion method based on a maximum likelihood estimator (MLE) when it is used in the PNS, which is approximately taking the mean value of array readings, the proposed method is equivalent to adaptively fusing the array readings and thus improves the pedestrian tracking accuracy. To compare the proposed method with MLE, two different types of walking tracks are designed. The 161 m straight line experiments show that the end position by ADR/modified ZUPT method is much closer to the one of the reference trajectory compared with the MLE in repeated walks, and the closed-loop tracks about 300 m show that the positioning error with respect to the total traveled distance is less than 0.6% (1σ), which is higher than 1% (1σ) of MLE.

Foot-Mounted Pedestrian Navigation Method Based on Gait Classification for Three-Dimensional Positioning

A foot-mounted pedestrian navigation system (PNS) that uses MEMS inertial measurement units (IMUs) can track the position of a person when walking normally on two-dimensional (2D) plane. However, the positioning accuracy is decreased under complex gaits. This article proposes a pedestrian navigation method based on gait classification for three-dimensional (3D) positioning. The commonly used gaits are divided into seven types and a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) is constructed as gait classifier. When the detected gaits are walking sideways or running, the step length models are built to calculate single step length. Otherwise the inertial navigation algorithm with zero velocity update (ZUPT) is used to calculate travel distance. If the pedestrian is going upstairs or downstairs, a barometer together with stair height constraint is applied to obtain altitude variation. Meanwhile, the heading drift is reduced combining zero integrated heading rate (ZIHR) method and simplified heuristic drift reduction (HDR) method. The gait classification results show that the classification accuracy of designed BLSTM-RNN reaches 99.4%. The results of mixed gaits experiment and multi-floor experiment show that the positioning errors of 2D and 3D trajectories are less than 2%.

Foot-Mounted Pedestrian Navigation Algorithm Based on BOR/MINS Integrated Framework

The traditional pedestrian navigation system that uses zero velocity update algorithm cannot calculate traveled distance accurately or observe the heading error. A new model called body odometer (BOR) that consists of a step length model and a correction factor is proposed to obtain precise single step length for dead reckoning. A BOR/MINS integrated framework uses the difference between micro electro mechanical inertial navigation system (MINS) calculated distance and single step length, as a new observation to estimate the correction factor and compensate navigation errors via a Kalman filter. To eliminate the heading error accumulation, a new gyro drift reduction method that combines heuristic drift reduction method and complementary filter is presented. The 200 m straight line experiments show that the calculated distance by BOR/MINS integrated method is much closer to the real distance with the average error percentage of 0.24%. Three differently designed trajectories’ experiments show that the proposed method has a higher match degree with the real trajectories and the positioning error with respect to the total traveled distance is less than 0.6%.

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

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.

A Novel Pedestrian Navigation Algorithm for a Foot-Mounted Inertial-Sensor-Based System.

This paper proposes a novel zero velocity update (ZUPT) method for a foot-mounted pedestrian navigation system (PNS). First, the error model of the PNS is developed and a Kalman filter is built based on the error model. Second, a novel zero velocity detection algorithm based on the variations in speed over a gait cycle is proposed. A finite state machine including three states is employed to model a gait cycle. The state transition conditions are determined based on speed using a sliding window. Third, the ZUPT software flow is illustrated and described. Finally, the performances of the proposed method and other methods are examined and compared experimentally. The experimental results show that the mean relative accuracy of the proposed method is 0.89% under various motion modes.

Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System

Zero velocity updates (ZUPT) is an effective way for the foot-mounted inertial pedestrian navigation systems. For the ZUPT technique to work properly, it is necessary to correctly detect the stance phase of each gait cycle. An adaptive stance-phase detection method is proposed based solely on an inertial sensor, which deals with the measurement fluctuations in swing and stance phases differently, and applies a clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait phases. The roles of the detection parameters and the relationship between them are analyzed to offer some suggestions for parameter tuning. Detection performance is evaluated with multisubject experimental data collected at varying walking speeds. The evaluation results show that the proposed detection method performs well in the presence of measurement fluctuations, which can make the detection of stance phases more robust and the choice of detection parameters more flexible.

Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

Abstract In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.

Foot-mounted Pedestrian Navigation based on Particle Filter with an Adaptive Weight Updating Strategy

The algorithm flow of an inertial-based Pedestrian Navigation System (PNS) can be divided into a trajectory-generation stage and trajectory-calibration stage. The Zero-velocity UPdaTe (ZUPT)-aided Extended Kalman Filter (EKF) algorithm is commonly used to resolve the trajectory of a walking person, but it still suffers from long-term drift. Many methods have been developed to suppress these drifts and thus to calibrate the trajectory generated by the previous stage. However, these methods have certain requirements, such as explicit map information or frequent location revisits, which are hard to satisfy in such situations as Search and Rescue (SAR) operations. A new approach is proposed in this paper that requires no explicit presupposition. This approach is based on a particle filter framework, with the weight of particles being adaptively adjusted according to the a priori knowledge of building structures and human behaviours. The distribution of particle weights is designed with awareness of the regular structures of buildings. The time-varying parameter of the distribution is acquired from a Hidden Markov Model (HMM) based on the foregoing odometry, which has a close relation with human behaviour. HMM is trained offline based on samples acquired in advance. Many real-world experiments under various scenarios were performed, and the results indicate good accuracy and robustness of the proposed approach.