scholarly journals Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5084 ◽  
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Hybrid System ◽  
Indoor Localization ◽  
Simultaneous Localization And Mapping ◽  
Maximum Error ◽  
Measurement Unit ◽  
Localization Error ◽  
Positioning Systems ◽  
Localization System ◽  
Localization And Mapping ◽  
Localization Systems

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

3D simultaneous localization and mapping using IMU and its observability analysis

Robotica ◽  
2010 ◽  
Vol 29 (6) ◽  
pp. 805-814 ◽  
Author(s):  
Farhad Aghili
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Measurement Unit ◽  
Adaptive Kalman Filter ◽  
Sensitivity Matrix ◽  
Process Noise ◽  
3 Dimensional ◽  
Observability Analysis ◽  
Localization And Mapping ◽  
Straight Line ◽  
Self Tuning

SUMMARYThis paper investigates 3-dimensional (3D) Simultaneous Localization and Mapping (SLAM) and the corresponding observability analysis by fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF). In addition to the vehicle's states and landmark positions, the self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix and the observation sensitivity matrix are derived in closed form, making it suitable for real-time implementation. Examination of the observability of the 3D SLAM system leads to the the conclusion that the system remains observable, provided that at least three known landmarks, which are not placed in a straight line, are observed.

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

scholarly journals End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi

2021 ◽  
Author(s):  
Mukhamet Nurpeiissov ◽  
Askat Kuzdeuov ◽  
Aslan Assylkhanov, ◽  
Yerbolat Khassanov ◽  
Hüseyin Atakan Varol
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Sampling Rate ◽  
Reference Points ◽  
Measurement Unit ◽  
Localization System ◽  
Practical Applications ◽  
External Data ◽  
End To End ◽  
Commercial Off The Shelf

This paper addresses sequential indoor localization using WiFi and Inertial Measurement Unit (IMU) modules commonly found in commercial off-the-shelf smartphones. Specifically, we developed an end-to-end neural network-based localization system integrating WiFi received signal strength indicator (RSSI) and IMU data without external data fusion models. The developed system leverages the advantages of WiFi and IMU modules to locate finer-level sequential positions of a user at 150 Hz sampling rate. Additionally, to demonstrate the efficacy of the proposed approach, we created the IMUWiFine dataset comprising IMU and WiFi RSSI readings sequentially collected at fine-level reference points. The dataset contains 120 trajectories covering an aggregate distance of over 14 kilometers. We conducted extensive experiments using deep learning models and achieved a mean error distance of 1.1 meters on an unseen evaluation set, which makes our approach suitable for many practical applications requiring meter-level accuracy. To enable experiment and result reproducibility, we made the developed localization system and IMUWiFine dataset publicly available in our GitHub repository.<br>

scholarly journals Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Localization Error ◽  
K Nearest Neighbor ◽  
Current Location ◽  
Smartphone Sensors ◽  
The Impact ◽  
Localization Systems

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

scholarly journals Accurate Localization in Acoustic Underwater Localization Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 762
Author(s):  
Gianni Cario ◽  
Alessandro Casavola ◽  
Gianfranco Gagliardi ◽  
Marco Lupia ◽  
Umberto Severino
Keyword(s):  
Statistical Analysis ◽  
Measurement Unit ◽  
Design Parameters ◽  
Final Position ◽  
Localization System ◽  
Sources Of Error ◽  
Accurate Localization ◽  
Hardware System ◽  
Underwater Localization ◽  
Localization Systems

In underwater localization systems several sources of error may impact in different ways the accuracy of the final position estimates. Through simulations and statistical analysis it is possible to identify and characterize such sources of error and their relative importance. This is especially of use when an accurate localization system has to be designed within required accuracy prescriptions. This approach allows one to also investigate how much these sources of error influence the final position estimates achieved by an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment used to simulate real acoustic underwater localization systems. The paper intends to analyze the main parameters that significantly influence the position estimates achieved by a Short Baseline (SBL) system. Specifically, the results of this analysis are presented for a proprietary localization system constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target. The simulator here presented has the purpose of simulating the hardware system and modifying some of its design parameters, such as the base-line length and the errors on the GPS and Inertial Measurement Unit (IMU) units, in order to understand which parameters have to modify for improving the accuracy of the entire positioning system. It is shown that statistical analysis techniques can be of help in determining the best values of these parameters that permit to improve the performance of a real hardware system.

scholarly journals Noise reduction for radio map crowdsourcing building in WLAN indoor localization system

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Liye Zhang ◽  
Zhuang Wang ◽  
Xiaoliang Meng ◽  
Chao Fang ◽  
Cong Liu
Keyword(s):  
Indoor Localization ◽  
Sensor Data ◽  
Mode Switching ◽  
Map Building ◽  
Position Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Straight Line ◽  
Radio Map ◽  
Localization Systems

AbstractRecent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

WiCLoc: A Novel CSI-based Fingerprint Localization System

2019 ◽  
Vol 34 (04) ◽  
pp. 2058003
Author(s):  
Lei Zhang ◽  
Yanjun Hu ◽  
Yafeng Liu ◽  
Jiaxiang Li ◽  
Enjie Ding
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Smart Devices ◽  
Fingerprint Matching ◽  
Wifi Networks ◽  
Localization System ◽  
Exhibition Hall ◽  
Localization Systems

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

scholarly journals A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 452 ◽  
Author(s):  
Thomas Tegou ◽  
Ilias Kalamaras ◽  
Markos Tsipouras ◽  
Nikolaos Giannakeas ◽  
Kostantinos Votis ◽  
...  
Keyword(s):  
Older People ◽  
Indoor Localization ◽  
Low Cost ◽  
Clinical Status ◽  
Localization System ◽  
Frailty Status ◽  
Asset Tracking ◽  
Commercial Applications ◽  
Tracking Device ◽  
Localization Systems

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).

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