scholarly journals Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Sci ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 7 ◽  
Author(s):  
Mickaël Delamare ◽  
Remi Boutteau ◽  
Xavier Savatier ◽  
Nicolas Iriart
Keyword(s):  
Motion Capture ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Ground Truth ◽  
Industrial Applications ◽  
Ultra Wideband ◽  
Complex Environments ◽  
Experimental Protocol ◽  
Localization Systems ◽  
Do So

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

scholarly journals Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Sci ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 62 ◽  
Author(s):  
Mickael Delamare ◽  
Remi Boutteau ◽  
Xavier Savatier ◽  
Nicolas Iriart
Keyword(s):  
Motion Capture ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Ground Truth ◽  
Industrial Applications ◽  
Ultra Wideband ◽  
Complex Environments ◽  
Experimental Protocol ◽  
Localization Systems ◽  
Do So

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

scholarly journals Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Mickaël Delamare ◽  
Remi Boutteau ◽  
Xavier Savatier ◽  
Nicolas Iriart
Keyword(s):  
Motion Capture ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Ground Truth ◽  
Industrial Applications ◽  
Ultra Wideband ◽  
Complex Environments ◽  
Experimental Protocol ◽  
Localization Systems ◽  
Do So

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

scholarly journals Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3431
Author(s):  
Mateusz Groth ◽  
Krzysztof Nyka ◽  
Lukasz Kulas
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Low Cost ◽  
Indoor Environments ◽  
Calibration Free ◽  
Architecture And Design ◽  
Positioning Algorithm ◽  
Parasitic Array ◽  
Better Than ◽  
Localization Systems

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

scholarly journals Self-calibration and Collaborative Localization for UWB Positioning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Matteo Ridolfi ◽  
Abdil Kaya ◽  
Rafael Berkvens ◽  
Maarten Weyn ◽  
Wout Joseph ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Mobile Nodes ◽  
Positioning Systems ◽  
Self Calibration ◽  
Significant Research ◽  
Anchor Nodes ◽  
Collaborative Localization ◽  
The Cost ◽  
Localization Systems

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

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 RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

Knowledge sharing, complex environments and small-worlds

2005 ◽  
Vol 24 (3) ◽  
pp. 185-195
Author(s):  
Mike Metcalfe
Keyword(s):  
Knowledge Sharing ◽  
System Thinking ◽  
Small Worlds ◽  
Complex Environments ◽  
Complex Environment ◽  
Strategic Response ◽  
Social Species ◽  
The Social ◽  
Self Organisation ◽  
Do So

This paper is about knowledge sharing vision appropriate for a complex environment. In these environments, traditional views of knowledge sharing as informing a hierarchical, centralised leadership may be misleading. A complex environment is defined as one that emerges unpredictable changes that require organisations to reconnect, to reorganise. Organisations need to be able to rapidly reconnect relationships so as to reflect new priorities, and to do so without causing change “bottlenecks”. The empirical biologists have observed that some social species have evolved structures that enable them to do this automatically what ever the environmental change. These organisational forms have survived for millions of years without central planning; rather they use local knowledge is reconnect as required overall providing an appropriate strategic response. These organisational forms seem to result from the small-worlds phenomenon and it is self organising. Specifically, this paper will argue that this small-worlds, self organisation, phenomena is a useful vision for designing a knowledge sharing vision appropriate for a complex environment. The supportive evidence is provided in the form of identifying the empirical attributes of self organisation and small worlds to provide an explanation of how and why it works. The system thinking, biology (insect) and the social-network literature are used.

Deep learning approach for UHF RFID-based indoor localization

2021 ◽  
pp. 1-13
Author(s):  
Haishu Ma ◽  
Zongzheng Ma ◽  
Lixia Li ◽  
Ya Gao
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Estimation Error ◽  
Research Direction ◽  
Linear Interpolation ◽  
Linear Mapping ◽  
Uhf Rfid ◽  
Indoor Location ◽  
Main Research

Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

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