Collaborative localization as a paradigm for incremental knowledge fusion

Analytical and simulation models for collaborative localization

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.

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.

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.

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.

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 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 this paper, a map-matching (MM) algorithm which combines an estimated position with digital road data is proposed. The presented algorithm using a virtual track is appropriate for a MEMS-based pedestrian dead reckoning (PDR) system, which can be used in mobile devices. Most of the previous MM algorithms are for car navigation systems and GPS-based navigation system, so existing MM algorithms are not appropriate for the pure DR-based pedestrian navigation system. The biggest problem of previous MM algorithms is that they cannot determine the correct road segment (link) due to the DR characteristics. In DR-based navigation system, the current position is propagated from the previous estimated position. This means that the MM result can be placed on a wrong link when MM algorithm fails to decide the correct link at once. It is a critical problem. Previous algorithms never overcome this problem because they did not consider pure DR characteristics. The MM algorithm using the virtual track is proposed to overcome this problem with improved accuracy. Performance of the proposed MM algorithm was verified by experiments.

Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult to adapt to changes in pedestrian walking speed, which leads to a rapid increase in the error of the PDR. In this paper, a new deep-learning model based on long short-term memory (LSTM) and Transformer, LT-StrideNet, is proposed to estimate pedestrian-stride length. Next, a shank-mounted PDR framework is built based on the proposed stride-length-estimation method. In the PDR framework, the detection of pedestrian stride is achieved by peak detection with a dynamic threshold. An extended Kalman filter (EKF) model is adopted to fuse the gyroscope, accelerometer, and magnetometer. The experimental results show that the proposed stride-length-estimation method can effectively adapt to changes in pedestrian walking speed, and our PDR framework has excellent positioning performance.

Complementary data are necessary to bind the positioning error growth of Pedestrian Dead Reckoning (PDR). In this paper, absolute position updates are made possible with the online detection of different types of points of interest (POIs) located on the map. The POIs are selected depending on specific motion patterns which are associated to absolute locations on the map. To create the POIs database, the correlation between pedestrian motion and different map locations is first studied and the outcome is a typology of POIs. A K-NN (K nearest neighbors) algorithm is used to train different motion modes, which are further exploited for the detection of POIs in order to update the PDR algorithm with position data. Experimental assessment of the POI-based PDR calibration is conducted in both outdoor and indoor spaces with a focus on the transition between both environments. 90% of the time, motion is correctly classified and the PDR position is corrected with an accuracy that depends on POIs features (width of corridor/door, staircase size…). Therefore, the approach is found to be promising for enhancing PDR positioning using only map data.

Navigation applications and location-based services are currently becoming standard features in smart phones with built-in GPS receivers. However, a ubiquitous navigation solution which locates a mobile user anytime anywhere is still not available, especially in Global Navigation Satellite System (GNSS) degraded and denied environments. Different motion sensors and angular sensors have been adopted for augmenting the positioning solutions for such environments. An electromyography (EMG) sensor, which measures electrical potentials generated by muscle contractions from human body, is employed in this paper to detect the muscle activities during human locomotion and captures the human walking dynamics for motion recognition and step detection in a Pedestrian Dead Reckoning (PDR) solution. The work presented in this paper is a consecutive step of our pilot studies in developing a novel and robust PDR solution using wearable EMG sensors. The PDR solution includes standing and walking identification, step detection, stride length estimation, and a position calculation with a heading angular sensor. A situation of standing still is identified from the EMG signals collected from a walking process, which has standing and walking dynamics, via a hidden Markov model classifier fed by sample entropy features. Such pre-classified processing reduces the misdetection rate of step detection. After step detection, two stride length estimation methods are investigated for the PDR solution. Firstly, a linear stride length estimation method based on statistic models is investigated to improve the accuracy of the PDR solution. Secondly, five different walking motions are recognized by a motion recognition algorithm based on some particular EMG features, and a fixed stride length is then set for each walking motion to propagate the position. To validate the effectiveness and practicability of the methods mentioned above, some field tests were conducted by a few testers. The test results indicate that the performance of the proposed PDR solution is comparable to that of a commercial GPS receiver in outdoor test under an open-sky environment.