Advanced Heuristic Drift Elimination for indoor pedestrian navigation

The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.

Indoor positioning is currently a research hotspot. In recent years, Pedestrian Dead Reckoning (PDR) has been widely used in indoor positioning. However, the positioning error caused by heading drifts will accumulate as the walking distance increases, so some methods need to be used to correct the heading angle. Heuristic Drift Elimination (HDE) is an effective heading correction algorithm, which only uses the information of a building’s dominant directions to reduce the heading error, but it does not apply to the non-dominant direction condition. In this paper, we propose a heading drift suppressing method for the limitation of HDE. Firstly, the method constructs membership functions to judge the pedestrian’s motion according to the result of comprehensive evaluation. Then, it further determines by a threshold condition whether the pedestrian walks along the dominant directions, and a heading error measurement is introduced for heading correction. Finally, we verify by experiments that the proposed method can correct heading angles properly for different conditions, which indicates an adaptability to the environment.

A pedestrian dead-reckoning (PDR) system based on micro-inertial technology usually uses a sensor integrated with a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and is attached to the person's body (usually on the foot) to acquire the trajectory of the pedestrian. Although a variety of PDR system solutions have been proposed, the PDR solution for marking time and walking hybrid motions has not been formally proposed and solved. Therefore, in this paper, we propose the PDR system using a step-and-heading system scheme for marking time and walking hybrid motion. The main contributions include: 1) an adaptive gait partitioning method for walking and marking time hybrid motions is proposed; 2) a motion classification algorithm that uses a multi-layer perceptron to classify whether each step is walking or marking time is proposed; and 3) a new step length estimator based on several currently proposed models is proposed. Finally, an extended Kalman filtering algorithm combining heuristic heading reduction, flat-ground hypothesis, and cardinal heading aided inertial navigation techniques is used for heading estimation. We evaluate the effectiveness of the proposed algorithm through experiments, and the experimental results show that the average position error of the two groups of test experiments based on marking time and walking hybrid motion is 0.42% and 0.6%, respectively.

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.

Indoor localization has become a popular topic in recent years. While self-contained pedestrian dead reckoning (PDR) systems can be conveniently implemented on a smartphone with built-in inertial sensors for indoor localization, the error of the estimated position for a PDR system can accumulate quickly and results in an unacceptable position accuracy. To address this issue, we propose the collaborative pedestrian dead reckoning (CPDR) system. The main idea of the CPDR system is when users are near to each other, we can leverage the proximity information to improve their estimated positions by means of the opportunistic Kalman filter. In addition, the backward correction scheme is used to improve the accuracy of user's trajectory. To evaluate the CPDR system, a prototype is implemented on Apple's iPhone 5. The experiment results show that the CPDR system achieves a better position accuracy than the raw PDR system.

Due to the high demand of location-based services in buildings, various indoor positioning methods have been proposed. Among them, the Pedestrian Dead Reckoning (PDR) systems have received much attention because of the widespread deployment of smartphones and no requirement of infrastructure. In this paper, we propose a PDR system to track a pedestrian holding a smartphone while he/she walks in a building. To eliminate the inevitable accumulated error over time, we suggest a method of using location related information from environments. We named it ‘landmark’, which is defined as a specific area (or point) where the pedestrian passes by in a building such as a stair, a door, a corner or a crossroad. If the system detects a landmark during the walking period, then it will correct the position of the user with the pre-defined position information of the landmark. In this work, we propose one kind of landmark called the door landmark. We have developed a method to detect the landmarks only using the sensors embedded in smartphones. The door landmark can be detected by combining the features from the magnetic sensor and user’s walking behavior. Via a set of experiments, we have evaluated the performances of the landmark detection and the position tracking of the system.

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

There are always frustrations for drivers in finding parking space and parking is costly in almost every major city in the world. In this paper, we propose a crowdsourcing solution by exploiting sensors in the smartphone to collect real-time parking availability information. In particular, we utilise a pedestrian dead reckoning (PDR) system to track the driver's trajectory to detect when he is about to leave his parking space. However, the effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we implement a waist-mounted-based PDR method on a smartphone that can measure the user's moving distance with a high accuracy. Furthermore, we design a map-matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

In this paper, a simultaneous localization and mapping algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning. The pedestrian dead-reckoning is computed using an extended Kalman filter with zero-velocity updates. The algorithm is validated using experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments.

The vision-aided Pedestrian Dead Reckoning (PDR) systems have become increasingly popular, thanks to the ubiquitous mobile phone embedded with several sensors. This is particularly important for indoor use, where other indoor positioning technologies require additional installation or body-attachment of specific sensors. This paper proposes and develops a novel 3D Passive Vision-aided PDR system that uses multiple surveillance cameras and smartphone-based PDR. The proposed system can continuously track users’ movement on different floors by integrating results of inertial navigation and Faster R-CNN-based real-time pedestrian detection, while utilizing existing camera locations and embedded barometers to provide floor/height information to identify user positions in 3D space. This novel system provides a relatively low-cost and user-friendly solution, which requires no modifications to currently available mobile devices and also the existing indoor infrastructures available at many public buildings for the purpose of 3D indoor positioning. This paper shows the case of testing the prototype in a four-floor building, where it can provide the horizontal accuracy of 0.16m and the vertical accuracy of 0.5m. This level of accuracy is even better than required accuracy targeted by several emergency services, including the Federal Communications Commission (FCC). This system is developed for both Android and iOS-running devices.

In order to improve the performance of the indoor localization system, the fusion of multi-source data is a common approach. For example, one can improve the WiFi localization accuracy on smartphones by combining pedestrian dead reckoning (PDR) results obtained through inertial sensors embedded in smartphones. Though obvious improvement in localization can be achieved, the existing methods do not sufficiently exploit the advantages of two data sources. To be specific, the existing studies directly fuse WiFi localization results and PDR results at a high level, i.e., the final coordinates of the WiFi localization system integrate with the final coordinates of PDR by certain algorithms, but ignores their relationship at a low level, i.e, the heading of the PDR , not its location results, is improved by the help of the WiFi localization. In addition, it is acknowledged that the pedestrian heading is the major source determining the performance of PDR. Therefore, this paper proposes to design a novel pedestrian heading estimation by fusing PDR and WiFi at a low level. Different from the traditional method, which employs a magnetometer to eliminate the drifts of a gyroscope, the method utilizes only the gyroscope of a smartphone for the heading estimation and relies on the WiFi localization trajectory in the fusion to compensate for the drift errors of the gyroscope-based heading estimation. In our algorithm, firstly, a pedestrian's activities trajectory is segmented into several straight paths with the help of the gyroscope of a smartphone. Secondly, the WiFi fingerprint localization coordinates falling into the time window of each straight path are fitted by the least-squares linear regression method. Lastly, the deviations of the gyroscope heading estimation of the smartphone when pedestrians walk in a straight direction are mitigated using the fitting slope obtained by the WiFi localization. Extensive experimental results demonstrate that our proposed algorithm can efficiently estimate the heading of pedestrians, and effectively reduce the cumulative errors of the gyroscope-based heading estimation using smartphones. In our experiments, the average error of the heading for pedestrians in 294 steps was reduced from 24.3 degrees to 1.22 degrees. Not requiring a magnetometer, our algorithm can reduce the drift errors of the heading estimation of pedestrians, achieve the deeper fusion of multi-source data in the fusion of WiFi and PDR, and potentially improves the endurance of smartphones.

Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive. Some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization, which does not require the deployment of beacon nodes. In a PDR system, a small number of sensors are put on the pedestrian. These sensors (such as a G-sensor and gyroscope) are used to estimate the distance and direction that a user travels. The effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we propose a novel waist-mounted based PDR that can measure the user's step lengths with a high accuracy. We utilize vertical acceleration of the body to calculate the user's change in height during walking. Based on the Pythagorean Theorem, we can then estimate each step length using this data. Furthermore, we design a map matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98.26% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

In this paper, we present an implementation of indoor localization, where the location of an object is determined without using the outdoor infrastructure of GPS. Various work involving different technologies exist, but the field remains open to a solution which can attain widespread use, in consideration of cost and infrastructure needed for deployment. For our implementation, we localized a common smartphone in an area with many WiFi signals. The main method was through RSS fingerprinting which was able to achieve a mean error of 2.5 m, aside from outliers that drove the error to 16 m. To improve this, Pedestrian Dead Reckoning (PDR) techniques were leveraged in addition to the WiFi system. Realtime testing showed that the WiFi+PDR system was able to achieve a 2.28 m mean localization error, while it reached 2.62 m for the WiFi-only approach. Finally, a peer-to-peer approach was explored, in which neighboring smartphone peers were treated as pseudo access points to help eliminate the outlier errors that resulted from very similar RSS fingerprints. To measure distance between peers, an acoustic method was employed using 1 KHz sine waves. The Time Difference of Arrival (TDoA) was computed between peers, and the readings allowed for coarse estimates in 5-m bins. However, for very noisy environments, this frequency range may cause the acoustic system to be erroneous, possibly driving the error to as high as 18.01 m.

Collaborative localization is the computation of improved spatial coordinates in mobile agents based on their physical meetings in a pedestrian dead reckoning (PDR) system. Upon meeting the agents can exchange information about their subjective position and update it based on a simple algorithm. We show in a simulation model that the localization error diverges unless this algorithm is introduced in which case it remains bounded. We consider collaborative localization as an example of broader incremental knowledge fusion and discuss its various implications such as the importance of well-informed agents.

Analytical and simulation models for collaborative localization