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 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 Feedforward ins aiding: an investigation of maneuvers for in-flight alignment

2007 ◽  
Vol 18 (4) ◽  
pp. 459-470 ◽  
Author(s):  
Jacques Waldmann
Keyword(s):  
Autonomous Vehicles ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Model Mismatch ◽  
External Data ◽  
Rate Gyro ◽  
External Position ◽  
The Impact ◽  
Gyro Drift

Navigation in autonomous vehicles involves integrating measurements from on-board inertial sensors and external data collected by various sensors. In this paper, the computer-frame velocity error model is augmented with a random constant model of accelerometer bias and rate-gyro drift for use in a Kalman filter-based fusion of a low-cost rotating inertial navigation system (INS) with external position and velocity measurements. The impact of model mismatch and maneuvers on the estimation of misalignment and inertial measurement unit (IMU) error is investigated. Previously, the literature focused on analyzing the stripped observability matrix that results from applying piece-wise constant acceleration segments to a stabilized, gimbaled INS to determine the accuracy of misalignment, accelerometer bias, and rate-gyro drift estimation. However, its validation via covariance analysis neglected model mismatch. Here, a vertically undamped, three channel INS with a rotating IMU with respect to the host vehicle is simulated. Such IMU rotation does not require the accurate mechanism of a gimbaled INS (GINS) and obviates the need to maneuver away from the desired trajectory during in-flight alignment (IFA) with a strapdown IMU. In comparison with a stationary GINS at a known location, IMU rotation enhances estimation of accelerometer bias, and partially improves estimation of rate-gyro drift and misalignment. Finally, combining IMU rotation with distinct acceleration segments yields full observability, thus significantly enhancing estimation of rate-gyro drift and misalignment.

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.

scholarly journals A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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