New Map-Matching Algorithm Using Virtual Track for Pedestrian Dead Reckoning

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.

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.

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.

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.

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.

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.

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.

With the increasing availability of GPS-equipped mobile devices, location-based services have become an integral part of everyday life. Among one of the initial steps of positioning data management, map matching aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate route based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. In this work, we propose a novel feature-based map matching algorithm that estimates the cost of a candidate route based on both GPS observations and human factors. To take human factors into consideration is highly important, especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated both the offline and the online versions of our proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60s ∼ 300s) and challenging track features (e.g., u-turns and loops). Measurements including map matching accuracy and system efficiency have been thoroughly evaluated and discussed. Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% (either offline or online with the window size set to 360s), with a slight increase in terms of the processing time. The experimental results show that our proposed method obtains the state-of-the-art map matching results in all the different combinations of sampling rates and challenging features.

This paper proposes two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure. As to the PDR approach, a more effective multi-threshold step detection algorithm is used to improve the positioning accuracy. According to pedestrians’ different walking patterns such as walking or running, this paper makes a comparative analysis of multiple step length calculation models to determine a linear computation model and the relevant parameters. In consideration of the deviation between the real heading and the value of the orientation sensor, a heading estimation method with real-time compensation is proposed, which is based on a Kalman filter with map geometry information. The corrected heading can inhibit the positioning error accumulation and improve the positioning accuracy of PDR. Moreover, this paper has implemented two positioning approaches integrated with Bluetooth and PDR. One is the PDR-based positioning method based on map matching and position correction through Bluetooth. There will not be too much calculation work or too high maintenance costs using this method. The other method is a fusion calculation method based on the pedestrians’ moving status (direct movement or making a turn) to determine adaptively the noise parameters in an Extended Kalman Filter (EKF) system. This method has worked very well in the elimination of various phenomena, including the “go and back” phenomenon caused by the instability of the Bluetooth-based positioning system and the “cross-wall” phenomenon due to the accumulative errors caused by the PDR algorithm. Experiments performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building in the China University of Mining and Technology (CUMT) campus showed that the proposed scheme can reliably achieve a 2-meter precision.

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.

Accuracy of independent GPS navigation systems is tied to the map-matching (MM) algorithm, which overcomes GPS error and traffic map error to map ve-hicle GPS tracks onto corresponding roads. This paper presents a map-matching process compatible with junction adjusting. An innovative idea is expressed in pa-per to improve the accuracy of intersection node at vector map by utilizing the large amounts of GPS trajectory data, which come from map-matching procedure. With the adaptively corrected digital map, the simulation result gave better map-matching effect, and showed the effectiveness of map-matching compatible with junction adjusting.

Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e.,latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. (C) 2013 American Society of Civil Engineers.

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.

GPS (Global Positioning System) has become an integral part of a vehicle system which provides speed, time, direction etc besides the navigation data. Speed is one of the primary attributes of vehicle accident. Many lives could have been saved if emergency service could receive accident information timely. This paper proposes to detect an accident from the map matched position of a vehicle by utilizing the GPS speed data and map matching algorithm and send accident location to an Alert Service Center. The GPS provides speed and position in every 0.1 second. The position data will be used in the map matching algorithm to locate the vehicle on the road. The present speed will be compared with the previous speed in every 0.1 second through a Microcontroller Unit. Whenever the speed will be falling below the safe calculated threshold speed, the system will generate an accident situation. It will check the vehicle location from map matching module and generate an accident situation if the vehicle is found outside the road network. This will reduce the false accident detection drastically. The map matched accident location is then sent by utilizing the GSM network. The proposed system will save many accident victims with timely rescue.