Inertial Navigation Systems and Its Practical Applications

Nawrat, Aleksander; Jędrasiak, Karol; Daniec, Krzysztof; Koteras, Roman

doi:10.5772/50578

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…There are other technologies that enable positioning a smart garment, like traditional cameras, infrared systems [101], ultrasounds [102], UWB [103] or inertial navigation systems [104].…”

Section: Communications Subsystemmentioning

confidence: 99%

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

2018

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Technology has become ubiquitous, it is all around us and is becoming part of us. Together with the rise of the Internet-of-Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet-of-Smart-Clothing.

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“…There are other technologies that enable positioning a smart garment, like traditional cameras, infrared systems [101], ultrasounds [102], UWB [103] or inertial navigation systems [104].…”

Section: Communications Subsystemmentioning

confidence: 99%

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

2018

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“…The accuracy of the GNSS deteriorates due to multi-path reflections and visibility issues under bridges and trees, in tunnels and urban canyons [3]. An Inertial Navigation System (INS) can be used to estimate the position and orientation of the vehicle during the GNSS outage periods provided the availability of initial orientation information [4]. Nevertheless, the INS, which is made up of sensors such as the accelerometer and gyroscope, suffers from an exponential error-drift during the double integration of the accelerometer's measurement to position and the integration of the gyroscopes attitude rate to orientation [5].…”

Section: Introductionmentioning

confidence: 99%

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

2021

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An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation.

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Device for People Detection and Tracking Using Combined Color and Thermal Camera

Woronow

Jędrasiak

Daniec

et al. 2023

Lecture Notes in Networks and Systems

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Inertial Navigation Systems and Its Practical Applications

Cited by 9 publications

References 6 publications

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

Device for People Detection and Tracking Using Combined Color and Thermal Camera

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