An Improved Heuristic Drift Elimination Method for Indoor Pedestrian Positioning

In this paper, we proposed Advanced Heuristic Drift Elimination (AHDE) which can remove azimuth drift error in indoor environments. In Pedestrian Dead Reckoning (PDR) system, azimuth error is one of the main factors that cause estimated position error. In order to reduce azimuth error, several methods are used. Heuristic Drift Elimination (HDE) algorithm proposed by Johann Borenstein shows great strength in indoor environments. HDE assumes that generally walls and corridors are straight and either parallel or orthogonal to each other in man-made building. They called the typical directions of walls and corridors as the dominant directions. HDE is corrected if the computed azimuth angle matches the closest dominant direction. HDE also has limitation when the pedestrian walks in various directions because HDE can cause a new azimuth error by matching the closed dominant direction. To overcome these limitations, we propose AHDE which is based on INS-EKF-ZUPT (IEZ) by using foot-mounted IMU. The algorithm consists with the following two steps. First, it determines whether a pedestrian is walking straight forward or not. If a pedestrian is not walking straight forward, the algorithm estimates the biases of accelerometers and gyroscopes by Zero velocity UPdaTe (ZUPT) method. However if the pedestrian is walking straight forward, the algorithm determines whether the pedestrian is walking along the dominant direction or not. When it is determined that pedestrian is walking along the dominant direction, the algorithm corrects the computed azimuth angle to the closest dominant direction. When it is determined that the pedestrian is not walking along the dominant direction but walking straight with no change in azimuth, AHDE applies a correction to the gyro output which contains the bias error. Experimental results show that the accuracy of AHDE is improved compared to HDE and the algorithm is a powerful method which can reduce the azimuth error in complex motion.

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.

In the cloud age, it is quite easy to collect sensory data from smartphones. With these sensory data, it is desired to provide various kinds of applications to serve the user. In this research, we aim at developing an indoor navigation system on smartphone using solely smartphone sensory data. There are many researches on indoor localization and navigation in the literature. Nevertheless, environmental sensors and/or wearable sensors are usually needed. This can be costly and inconvenient. In this paper, we propose a smartphone indoor localization system using only accelerometer and gyroscope data from the smartphone. The Pedestrian Dead Reckoning (PDR) approach is used to build this system. The PDR approach is simple and efficient though seems traditional. The major weakness of the PDR is that the estimation error would accumulate over time. Thus we propose to add so-called calibration marks which look like short arrows and are placed on both the floor plan and the ground. To use the system, the user first finds a calibration mark on the ground, stands on it and faces the right direction. He/she then moves the android icon (representing the user) on top of the calibration mark on the floor plan on the smartphone. When the user starts to move, the android icon also moves on the floor plan following the real-time estimation of step length and moving direction change for each step from accelerometer and gyroscope data. This is a prototype of an indoor navigation system that can become fully functional after an optimal path planning module is included. Experimental results of estimated walking trace tests show high accuracy. The system is promising and useful as long as a floor plan and calibration marks are built in advance.

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.

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

This study presents a method for analyzing workers’ behavior using indoor positioning technology and field test in the workplace. Recently, various indoor positioning methods, such as Wi-Fi, Bluetooth low energy (BLE), visible light communication, Japan’s indoor messaging system, ultra-wide band (UWB), and pedestrian dead reckoning (PDR), have been investigated. The development of these technologies allows tracking of movement of both people and/or goods in indoor spaces, people and/or goods behavior analysis is expected as one of the key technologies for operation optimization. However, when we use these technologies for human tracking, there are some problem as follows. 1) Many cases need to use dedicated facilities (e.g. UWB). 2) When we use smartphone as sensing device, battery depletion is one of the big problem (especially using PDR). 3) the accuracy is instability for tracking (e.g. Wi-Fi). Based on these matters, in this study we designed and developed an indoor positioning system using BLE positioning. And, we adopted smartphone for business use as sensing device, developed a smartphone application runs on android OS. Moreover, we conducted the field test of developed system at Itoki Corporation’s ITOKI Tokyo Innovation Center, SYNQA, office (Tokyo, Japan). Over 40 workers participated in this field test, and worker tracking log data were collected for 6 weeks. We analyzed the characteristics of the workers’ behavior using this log data as a prototyping.

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

Foot-mounted inertial sensor is a practical and efficient approach for indoor personnel positioning, because it requires neither pre-deployed infrastructure nor on-line “fingerprint” database. To suppress the heading drift of gyroscopes, magnetic field is often used as a stable orientation reference. But the magnetic field inside buildings changes rapidly while space and time changes, and it is also sensitive to some metal objects or electrical equipments. Thanks to the regular structures of most buildings, Heuristic Drift Elimination (HDE) techniques can be applied to suppress slowing-changed small-scale drifts between two steps, under the assumption that most of the paths in the buildings consist of straight lines and fixed angles. Inspired by the heuristic method, a Near Real-Time Heading Drift Correction (NRHDC) approach is proposed in this paper, which determines the current walking behavior based on the steps foregoing and following. Different step status are set to classify each step and different templates are trained for walking behavior discrimination. This approach is supposed to exploit more information and give more precise result. The experimental results in a building also demonstrate the effectiveness of the proposed approach, with superior accuracy to other methods.

Technology progression in the last decades leads to many smartphone advancements, such as embedding variety of sensors for various measurement and applications. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant developments. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; indoor navigation, asset tracking, controlling in warehouse, rescue operation, and entertainment applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to an existing unpredictable error of the embedded sensors. To solve this issue, fusing multi-sensors measurements is proposed, in this paper, to reduce the existing sensors drifts and errors. Further, the sensors' measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with higher accuracy. The obtained positioning accuracy through the proposed approach and based on trial experiments is 2 meters, which is equivalent to 10% improvement in comparison with state of the art.

Indoor localization using the magnetic fingerprint collected by the embedded sensors of smartphone has been under constant improvement with the widespread popularity of the smartphone. Most of the current systems rely on the Particle Filter (PF) or the Kalman Filter (KF) to combine the Pedestrian Dead Reckoning (PDR) with the magnetic or WiFi fingerprint for improving their accuracy. In this paper, a system using the PDR and the magnetic fingerprint is proposed for indoor localization, which is based on the combination of the Particle Filter (PF) and the Extend Kalman Filter (EKF). The system includes the Pedestrian Dead Reckoning (PDR) module and the magnetic fingerprint matching module. In particular, the hybrid indoor positioning algorithm which combines the Particle Filter (PF) and the Extend Kalman Filter (EKF) is proposed in the magnetic fingerprint matching module for the fusion of the results of the Pedestrian Dead Reckoning module and the magnetic fingerprint. This hybrid indoor positioning algorithm is the key component which can reduce the computation of the Particle Filter effectively and solve the inherent blindness and particles degeneration problem. The obtained results in the real scenarios show that our fusion system achieves better results than the widely adopted system in which the Particle Filter (PF) or the Kalman Filter (KF) is used. The evaluation shows the system achieves a localization accuracy about 1-2m on average in a large building.

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.

This chapter presents a cost-effective system for indoor localization and navigation that does not require the use of satellite positioning or data communication networks. The system, implemented as a smartphone app, relies on QR codes that are pre-generated and attached to the walls inside the building. By utilizing the information from these codes and the smartphone's inertial motion unit (IMU) sensors processed by the Pedestrian Dead-Reckoning (PDR) algorithm, the user's current position can be determined. The Dijkstra navigation algorithm is then used to guide the user to the desired destination. The smartphone app can also be used as a healthcare logistics service in mass-casualty incidents for collecting and reporting georeferenced triage decisions to the cloud.

In recent years, the demand for location based service has promoted the rapid development of pedestrian navigation and positioning technology. Among them, Pedestrian Dead Reckoning (PDR) based on inertial sensor can greatly improve the robustness of indoor and outdoor pedestrian navigation system due to its environment-friendly. This paper designs a robust PDR algorithm based on smart phone. This paper constructs multi-level constraints based on INS algorithm and establishing observation equation needed by EKF using the mutually complementary relationship among sensors, such as gyroscope accelerometer and magnetometer, and human dynamic model. The cumulative error of the system is less than 2% of the traveling distance when using different intelligent terminals, holding on hand both in indoor and outdoor test.

Recently, researchers have paid attention to designing indoor navigation services for smartphone users. Conventional indoor navigation systems highly rely on well-known indoor information and prior training phase for localization, and thus it is time- and labor-consuming to bootstrap indoor navigation services. Without too much prior configurations, the proposed indoor navigation system, called ezNavi , utilizes trajectory information (reported by users) to generate indoor pathway and point of interests (POIs). The proposed system consists of a front-end mobile application (APP) and a back-end server. The mobile APP infers users’ walking trajectories according to sensory values from smartphones. The back-end server processes crowdsourced trajectories with the help of deployed Bluetooth low energy beacon devices, and then produces pathways of the indoor environment. After obtaining more trajectories, the ezNavi can further refine the derived pathways to provide more efficient guidance services for users. Our experiment and prototyping results reveal that the ezNavi can effectively derive users’ walking trajectories, produces indoor pathways, and indicates directions for users.

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.