scholarly journals Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5031
Author(s):  
Humayun Khan ◽  
Adrian Clark ◽  
Graeme Woodward ◽  
Robert W. Lindeman
Keyword(s):  
Inertial Sensor ◽  
Vision System ◽  
Tracking System ◽  
Indoor Positioning ◽  
Fiducial Marker ◽  
Measurement Unit ◽  
Positioning System ◽  
Positioning Systems ◽  
Marker Tracking ◽  
Training Exercises

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

scholarly journals Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 83
Author(s):  
Keiichi Zempo ◽  
Taiga Arai ◽  
Takuya Aoki ◽  
Yukihiko Okada
Keyword(s):  
Radio Wave ◽  
Indoor Positioning ◽  
Measurement Range ◽  
Pulse Interval ◽  
Retail Stores ◽  
Positioning System ◽  
Radio Waves ◽  
Positioning Systems ◽  
Environment Recognition ◽  
Indoor Positioning System

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors.

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

scholarly journals An Indoor Positioning System Using Multiple Methods and Tools

2018 ◽  
Vol 4 (1) ◽  
pp. 11-22
Author(s):  
Michael Adeyeye Oshin ◽  
Nobaene Sehloho
Keyword(s):  
Wireless Mesh Network ◽  
Ad Hoc ◽  
Indoor Positioning ◽  
Mesh Network ◽  
Positioning System ◽  
Tracking Systems ◽  
Positioning Systems ◽  
Wireless Mesh ◽  
Positioning Method ◽  
Indoor Positioning System

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.

scholarly journals A robust mobile robot indoor positioning system based on Wi-Fi

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989666 ◽  
Author(s):  
Wei Cui ◽  
Qingde Liu ◽  
Linhan Zhang ◽  
Haixia Wang ◽  
Xiao Lu ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Principal Component ◽  
Indoor Positioning ◽  
Positioning System ◽  
Infrared Sensors ◽  
Positioning Systems ◽  
Indoor Mobile Robot ◽  
Robot Positioning ◽  
Learning Machine ◽  
Noisy Measurements

Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.

scholarly journals Lora RTT Ranging Characterization and Indoor Positioning System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiang Liu ◽  
XiuJun Bai ◽  
Xingli Gan ◽  
Shan Yang
Keyword(s):  
Estimation Method ◽  
Ground Truth ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Cumulative Distribution ◽  
Positioning System ◽  
Positioning Error ◽  
Positioning Systems ◽  
Ground Truth Location ◽  
Indoor Positioning System

In recent years, indoor positioning systems (IPS) are increasingly very important for a smart factory, and the Lora positioning system based on round-trip time (RTT) has been developed. This paper introduces the ranging characterization, RTT measurement, and position estimation method. In particular, a particle filter localization method-aided Lora pseudorange fitting correction is designed to solve the problem of indoor positioning; the cumulative distribution function (CDF) criteria are used to measure the quality of the estimated location in comparison to the ground truth location; when the positioning error on the x -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 61% and 99%, which are higher than the 32% and 85% without pseudorange correction. When the positioning error on the y -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 71% and 99.9%, which are higher than the 52% and 94.8% without pseudorange correction.

scholarly journals Indoor Location Tracking System Based on Android Application Using Bluetooth Low Energy Beacons for Ubiquitous Computing Environment

2020 ◽  
pp. 572-576
Author(s):  
Khamla NonAlinsavath ◽  
◽  
Lukito Edi Nugroho ◽  
Widyawan Widyawan ◽  
Kazuhiko Hamamoto
Keyword(s):  
Radio Frequency Identification ◽  
Tracking System ◽  
Signal Strength ◽  
Smartphone Application ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning System ◽  
Location Awareness ◽  
Android Application ◽  
Bluetooth Low Energy

Indoor positioning and tracking systems have become enormous issue in location awareness computing due to its improvement of location detection and positioning identification. The locations are normally detected using position technologies such as Global Positioning System, radio frequency identification, Bluetooth Beacon, Wi-Fi fingerprinting, pedometer and so on. This research presents an indoor positioning system based on Bluetooth low energy 4.0 Beacons; we have implemented Bluetooth signal strength for tracking the specific location and detect the movement of user through Android application platform. Bluetooth low energy was addressed to be an experiment technique to set up into the real environment of interior the building. The signal strength of beacons is evaluated and measured the quality of accuracy as well as the improvement of provide raw data from Beacons to the system to get better performance of the direction map and precise distance from current location to desire’s positioning. A smartphone application detects the location-based Bluetooth signal strength accurately and can be achieved the destination by provided direction map and distance perfectly.

scholarly journals Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

2021 ◽  
Author(s):  
Ryan Murari
Keyword(s):  
Neural Networks ◽  
Synthetic Data ◽  
Real Data ◽  
Signal Propagation ◽  
Indoor Positioning ◽  
Sensor Technology ◽  
Positioning System ◽  
Distance Measurements ◽  
Positioning Systems ◽  
Indoor Positioning System

With the increasing widespread of sensor technology, new solutions for indoor positioning systems are continuously being developed and with them, new services requiring accurate positioning data have seen a great rise in popularity. In this thesis, a new design technique and deployment methodology for an indoor positioning system using neural networks is proposed to offer more flexibility and simplicity in the development of such a system which is currently very context-bound. The usage of battery-powered tags implies also that systems should not require excessive power consumption and the large number of targets to position requires a method that is not only accurate but also scalable. The proposed positioning system utilizes a small “swarm” of neural networks tasked to position targets based on distance measurements from Ultrawide Band sensors and requires shorter fingerprint collection campaigns and enables more flexibility in system deployment and alterations. Instead of relying solely on real data collected on the field for the training of neural networks, synthetic data is used for an initial training phase. Together, these propositions allow flexibility in terms of adding, removing or altering positions of reference nodes and simplifies offline deployment operations of an indoor positioning system. This thesis presents a system operating in a laboratory-workshop environment capable of good positioning accuracies and maintains robust performances in poor signal propagation.

scholarly journals Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

2021 ◽  
Author(s):  
Ryan Murari
Keyword(s):  
Neural Networks ◽  
Synthetic Data ◽  
Real Data ◽  
Signal Propagation ◽  
Indoor Positioning ◽  
Sensor Technology ◽  
Positioning System ◽  
Distance Measurements ◽  
Positioning Systems ◽  
Indoor Positioning System

With the increasing widespread of sensor technology, new solutions for indoor positioning systems are continuously being developed and with them, new services requiring accurate positioning data have seen a great rise in popularity. In this thesis, a new design technique and deployment methodology for an indoor positioning system using neural networks is proposed to offer more flexibility and simplicity in the development of such a system which is currently very context-bound. The usage of battery-powered tags implies also that systems should not require excessive power consumption and the large number of targets to position requires a method that is not only accurate but also scalable. The proposed positioning system utilizes a small “swarm” of neural networks tasked to position targets based on distance measurements from Ultrawide Band sensors and requires shorter fingerprint collection campaigns and enables more flexibility in system deployment and alterations. Instead of relying solely on real data collected on the field for the training of neural networks, synthetic data is used for an initial training phase. Together, these propositions allow flexibility in terms of adding, removing or altering positions of reference nodes and simplifies offline deployment operations of an indoor positioning system. This thesis presents a system operating in a laboratory-workshop environment capable of good positioning accuracies and maintains robust performances in poor signal propagation.

Sign in / Sign up

Export Citation Format

Share Document