Pedestrian Navigation System Research Articles

Heading estimation algorithm for pedestrian navigation using a dual-foot-mounted IMU with a millimetre wave radar

Heading estimation is crucial in pedestrian navigation systems based on IMUs, but accuracy often suffers degradation due to error accumulation. This study introduces a novel heading estimation algorithm using dual-foot-mounted IMUs with a millimeter-wave radar to enhance accuracy. The algorithm includes a Single-foot Multiconditional Constraint Algorithm (SMC-A), adaptive step length constraint, and bipedal maximum heading difference constraint. Firstly, the SMC-A utilizes zero speed correction, zero angular rate updates, and self-observing heading error corrections through extended Kalman filtering. Then, the adaptive step length constraint limits pedestrian motion, and the bipedal constraint corrects heading errors. Finally, the Bipedal Multiconditional Constraint Algorithm (BMC-A) incorporates millimeter-wave radar measurements of foot distance. Experiments show heading deviations of 3.74° and 4.03° are obtained for the left and right feet over a 30-minute, 1.45 km trial. The new algorithm improves heading accuracy by 68.19 % and 70.67 % for the left and right feet, respectively, compared to traditional SMC-A.

An advanced adaptive algorithm driven by online blind noise level estimation for pedestrian positioning

An effective and universal positioning algorithm is essential for a highly reliable and accurate foot-mounted inertial pedestrian navigation system. Algorithm performance is significantly influenced by the accuracy of noise level estimation. This study introduces a novel blind noise estimation algorithm to reduce the impact of uncertain and variable system noise characteristics on positioning accuracy. The algorithm combines a quasi-population growth wavelet transform threshold with an adaptive window length adjustment strategy. Firstly, a quasi-population growth wavelet threshold function is employed to separate motion signals from noise and assess noise levels. Secondly, an adaptive sliding window strategy based on motion-noise correlation optimizing the balance between estimation precision and computational efficiency. These methods enable robust noise level estimation without prior knowledge and achieve real-time adjustments to the system noise matrix in Extended Kalman Filter of the inertial navigation system, thereby significantly enhancing navigation error suppression. The effectiveness of above methodologies has been experimentally validated.

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.

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%.

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.

A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors

A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors

A Novel Method for Extracting and Analyzing the Geometry Properties of the Shortest Pedestrian Paths Focusing on Open Geospatial Data

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the “dissimilarity ratio” was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the “sinuosity index”, a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman’s rank correlation showed a direct relationship between route curviness and route length, rS(758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators.

Neural inertial navigation system on pedestrian

The recent research shows that data-driven inertia navigation technology can significantly alleviate the drift error of micro-electro-mechanical system inertial measurement unit (MEMS-IMU) in pedestrian localization. However, most existing methods must rely on attitude information provided by external procedure (such as smartphone API), which violates the original intention of full autonomy of inertial navigation, and attitude information is also inaccurate. To address the problem, we propose a pedestrian indoor neural inertial navigation system that does not rely on external information and is only based on low-cost MEMS-IMU. First, a deep learning based neural inertial network was designed to estimate attitude. Then, in order to obtain position estimation with both global and local accuracy, an invariant extended Kalman filter (IEKF) framework was proposed, where 3D displacement and its uncertainty regressed by a deep residual network are utilized to update IEKF. Extensive experimental results on a public dataset and a self-collected dataset show that the proposed method provides accurate attitude estimation and outperforms state-of-the-art methods in position estimation, demonstrating the superiority of our method in reliability and accuracy.

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.

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.

HINNet: Inertial navigation with head-mounted sensors using a neural network

Human inertial navigation systems have been developing rapidly in recent years, and it has shown great potential for applications within healthcare, smart homes, sports, and emergency services. Placing inertial measurement units on the head for localisation is relatively new. However, it provides a very interesting option, as there are several everyday head-worn items that could easily be equipped with sensors. Yet, there remains a lack of research in this area and currently no localisation solutions have been offered that allow for free head-rotations during long periods of walking. To solve this problem, we present HINNet, the first deep neural network (DNN) pedestrian inertial navigation system allowing free head movements with head-mounted inertial measurement units (IMUs), which deploys a 2-layer bi-directional LSTM. A new ’peak ratio’ feature is introduced and utilised as part of the input to the neural network. This information can be leveraged to solve the issue of differentiating between changes in movements related to the head and those that are associated with the walking pattern. A dataset with 8 subjects totalling 528 min has been collected on three different tracks for training and verification. The HINNet could effectively distinguish head rotations and changes in walking direction with a distance percentage error of 0.46%, a relative trajectory error of 3.88 m, and a absolute trajectory error of 5.98 m, which outperforms the current best head-mounted Pedestrian Dead Reckoning (PDR) method.

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.

Novel pedestrian navigation system based on zero velocity update procedure technology and improved Sage‐Husa adaptive Kalman filter with index fading memory factor

AbstractTo acquire an accurate location on the occasions, such as in an indoor, tunnel, and valley, where satellite navigation signals fail. The paper designs a pedestrian navigation system by using the zero velocity update procedure technology (ZUPT) and Kalman filter to reduce the location error. The measurement noise characteristic (mean and variance) of the micro electro mechanical systems gyros is unknown and time variant, but in traditional studies, it is usually thought and calculated as a constant. So the optimality of the error estimation of the Kalman filter cannot be reached. To address this question, this paper proposes the improved Sage–Husa Adaptive Kalman Filter (SHAKF) based on the index fading memory factor to realise the state estimation of the Kalman filter and navigation error correction. The advantage of improved SHAKF is it can accurately estimate the state vector when the measurement noise is unknown and time variant. To verify the validity of novel navigation methods, walking experiments under outdoor environments and indoor environments are carried out. The results of actual walking experiments demonstrate that the proposed method can effectively reduce the pedestrian location error compared with the traditional ZUPT method. The mean location error is reduced by more than 10%, and the variance of the location error is reduced by more than 5%.

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.

Recent Advances in Pedestrian Inertial Navigation Based on Smartphone: A Review

Indoor location-based service is a hot research topic whose application market size is expected to grow from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 41 billion by 2022 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 58 billion by 2023. As a portable communication device, smartphones have inherent advantages (such as being an essential gadget in daily life, embedding various sensors, and having powerful computing and storage ability), which provides a great opportunity for human activity recognition and pedestrian navigation. Smartphone-based pedestrian inertial navigation systems (PINSs) utilize measurements from inertial sensors embedded in smartphones to reckon the pedestrian’s location. Compared with other indoor position technologies, PINS has the advantage of autonomy and continuity, and can achieve high-precision positioning in a short period. Though various PINS methods based on the smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PINS are relatively less so far in the literature. This article aims to provide an elaborated, timely, and valuable survey of different infrastructure-free pedestrian positioning and navigation techniques, including pedestrian inertial navigation methods, performance evaluation, and applications. Finally, current challenges and future research trends are discussed. This work propels a better understanding of existing pedestrian inertial positioning methods. It is helpful for researchers to further design and develop more accurate and robust pedestrian positioning and navigation systems based on inertial sensors embedded in the smartphone.

Landmark selection for route instructions: At which corner of an intersection is the preferred landmark located?

Cognitive studies showed that good landmarks–salient objects in the environment–make it easier for recipients of route instructions to find their way to the destination. Adding landmarks to route instructions also improves mobile navigation systems for pedestrians. But, which landmarks do people consider most helpful when giving route instructions? Four experiments explored this question. In the first experiment, the environment, including the route and landmarks, was presented on a map. The landmarks were located at the four corners of a right-angled intersection. Participants had to select those landmark-based route instructions they considered most helpful. In all other experiments, the environment was presented from an egocentric perspective, either in a video or as a sequence of pictures of intersections. Participants had to select those landmarks they would use in a route instruction. All landmarks had the same visual and semantic salience. The positions of the participants at the intersection were varied. Results show that participants consistently selected landmarks at the side of the road into which they had to turn. Moreover, the participants' position at the intersection affected whether they selected landmarks before or behind the decision point. These results have consequences for human spatial cognition research and for the automatic selection of landmarks in mobile pedestrian navigation systems.

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%.

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.