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Academic literature on the topic 'Inertial data'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Inertial data"

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Wicke, Jason, and Genevieve A. Dumas. "Estimating Segment Inertial Parameters Using Fan-Beam DXA." Journal of Applied Biomechanics 24, no. 2 (May 2008): 180–84. http://dx.doi.org/10.1123/jab.24.2.180.

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Body segment inertial parameters are required as input parameters when the kinetics of human motion is to be analyzed. However, owing to interindividual differences in body composition, noninvasive inertial estimates are problematic. Dual-energy x-ray absorptiometry (DXA) is a relatively new imaging approach that can provide cost- and time-effective means for estimating these parameters with minimal exposure to radiation. With the introduction of a new generation of DXA machines, utilizing a fan-beam configuration, this study examined their accuracy as well as a new interpolative data-reduction process for estimating inertial parameters. Specifically, the inertial estimates of two objects (an ultra-high molecular density plastic rod and an animal specimen) and 50 participants were obtained. Results showed that the fan-beam DXA, along with the new interpolative data-reduction process, provided highly accurate estimates (0.10–0.39%). A greater variance was observed in the center of mass location and moment of inertia estimates, likely as a result of the course end-point location (1.31 cm). However, using a midpoint interpolation of the end-point locations, errors in the estimates were greatly reduced for the center of mass location (0.64–0.92%) and moments of inertia (–0.23 to –0.48%).
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Sun, Ning, Jin Wang, and Fang Hua Lei. "A New Method to Measure Inertial Parameters of Rigid Body Based on Energy Decay Theory." Advanced Materials Research 146-147 (October 2010): 151–55. http://dx.doi.org/10.4028/www.scientific.net/amr.146-147.151.

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Most measuring methods used now can only measure the rigid body’s six inertial parameters like the moment of inertia, the product of inertia and the centre of inertia toward to two-dimensional reference system. So a new method which can measure all the nine inertial parameters toward to three-dimensional reference system is proposed. The moment of inertia of object rotating the axis is obtained by energy decay method. Through using the translation and rotation transformation theory of product of inertia, the formula of moment of inertia including the information of product of inertia and centre of inertia is deduced. Then equations are built to solve all the parameters. Furthermore, a measuring instrument is designed based on the aerostatic bearing. Results show that this new method is available and by analyzing the experimental data, suggestions are made to improve this measuring method.
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Wang, Zhe, Xisheng Li, Xiaojuan Zhang, Yanru Bai, and Chengcai Zheng. "Real-time location estimation for indoor navigation using a visual-inertial sensor." Sensor Review 40, no. 4 (June 10, 2020): 455–64. http://dx.doi.org/10.1108/sr-01-2020-0014.

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Purpose The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data. Design/methodology/approach First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia. Findings In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310. Originality/value This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.
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Ushaq, Muhammad, and Jian Cheng Fang. "An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems." Applied Mechanics and Materials 245 (December 2012): 323–29. http://dx.doi.org/10.4028/www.scientific.net/amm.245.323.

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Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.
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Gao, Zhenyi, Jiayang Sun, Haotian Yang, Jiarui Tan, Bin Zhou, Qi Wei, and Rong Zhang. "Exploration and Research of Human Identification Scheme Based on Inertial Data." Sensors 20, no. 12 (June 18, 2020): 3444. http://dx.doi.org/10.3390/s20123444.

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The identification work based on inertial data is not limited by space, and has high flexibility and concealment. Previous research has shown that inertial data contains information related to behavior categories. This article discusses whether inertial data contains information related to human identity. The classification experiment, based on the neural network feature fitting function, achieves 98.17% accuracy on the test set, confirming that the inertial data can be used for human identification. The accuracy of the classification method without feature extraction on the test set is only 63.84%, which further indicates the need for extracting features related to human identity from the changes in inertial data. In addition, the research on classification accuracy based on statistical features discusses the effect of different feature extraction functions on the results. The article also discusses the dimensionality reduction processing and visualization results of the collected data and the extracted features, which helps to intuitively assess the existence of features and the quality of different feature extraction effects.
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Gildeh, B. S., and S. Asghari. "Inertial capability index based on fuzzy data." International Journal of Metrology and Quality Engineering 2, no. 1 (2011): 45–49. http://dx.doi.org/10.1051/ijmqe/2011008.

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Svensson, A., and J. Holst. "Integration of Navigation Data." Journal of Navigation 48, no. 1 (January 1995): 114–35. http://dx.doi.org/10.1017/s0373463300012558.

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This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.
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Zhang, Shuang, Ada Zhen, and Robert L. Stevenson. "A Dataset for Deep Image Deblurring Aided by Inertial Sensor Data." Electronic Imaging 2020, no. 14 (January 26, 2020): 379–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.14.coimg-379.

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Recent work in image deblurring aided by inertial sensor data has shown promise. Separate work has also shown that deep learning techniques are useful for the image deblurring problem. Due to a lack of a proper dataset, however, deep learning techniques have not yet to be successfully applied to image deblurring when inertial sensor data is also available. This paper proposes to generate a synthetic training and testing dataset that includes groundtruth and blurry image pairs as well as inertial sensor data recorded during the exposure time of each blurry image. To simulate the real situations, the proposed dataset called DeblurIMUDataset considers synchronization issue, rotation center shift, rolling shutter effect as well as inertial sensor data noise and image noise. This dataset is available online.
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Molinari, John, Michaela Rosenmayer, David Vollaro, and Sarah D. Ditchek. "Turbulence Variations in the Upper Troposphere in Tropical Cyclones from NOAA G-IV Flight-Level Vertical Acceleration Data." Journal of Applied Meteorology and Climatology 58, no. 3 (March 2019): 569–83. http://dx.doi.org/10.1175/jamc-d-18-0148.1.

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AbstractThe NOAA G-IV aircraft routinely measures vertical aircraft acceleration from the inertial navigation system at 1 Hz. The data provide a measure of turbulence on a 250-m horizontal scale over a layer from 12.8- to 14.8-km elevation. Turbulence in this layer of tropical cyclones was largest by 35%–40% in the inner 200 km of radius and decreased monotonically outward to the 1000-km radius. Turbulence in major hurricanes exceeded that in weaker tropical cyclones. Turbulence data points were divided among three regions of the tropical cyclone: cirrus canopy; outside the cirrus canopy; and a transition zone between them. Without exception, turbulence was greater within the canopy and weaker outside the canopy. Nighttime turbulence exceeded daytime turbulence for all radii, especially within the cirrus canopy, implicating radiative forcing as a factor in turbulence generation. A case study of widespread turbulence in Hurricane Ivan (2004) showed that interactions between the hurricane outflow channel and westerlies to the north created a region of absolute vorticity of −6 × 10−5 s−1 in the upper troposphere. Outflow accelerated from the storm center into this inertially unstable region, and visible evidence for turbulence and transverse bands of cirrus appeared radially inward of the inertially unstable region. It is argued that both cloud-radiative forcing and the development of inertial instability within a narrow outflow layer were responsible for the turbulence. In contrast, a second case study (Isabel 2003) displayed strong near-core turbulence in the presence of large positive absolute vorticity and no local inertial instability. Peak turbulence occurred 100 km downwind of the eyewall convection.
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Nugroho, FX Satriyo Dwi. "Kajian Inertial Measurement Unit Berbasis Arduino Untuk Dokumentasi Digital Motion Capture Tarian Tradisional." Journal of Animation & Games Studies 2, no. 2 (January 18, 2017): 251. http://dx.doi.org/10.24821/jags.v2i2.1423.

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Visual digital documentation of traditional dance in Indonesia is still limited to photographs and videos recording. Motion capture technology has the potential to add more depth documenting traditional dances. This technology maps the position of the model (in this case the human body) and its motion in three dimensions. There are two popular ways in recording motion capture, using Vision Based Camera and Inertial measurement unit. Inertial Measurement Unit works by combining accelerometer and gyroscope to detect changes in the rotation axis relative lateral and angular. Those changes will be interpreted Arduino micro-controller platform as functions of motions that recorded as a motion capture data. Motion capture data that was obtained from traditional dance in Indonesia can be applied for many things such as education, standardization, documentation, and preservation of cultural assetsKeywords: digital documentatuion, motion capture, inertia measurement unit, angular relative, digital heritage. Abstrak Dokumentasi digital secara visual untuk tari tradisional di Indonesia masih terbatas pada perekaman secara fotografis dan videografis. Teknologi motion capture memiliki potensi untuk menambah kekayaan dokumentasi untuk tari tradisional. Teknologi ini memetakan posisi model (dalam hal ini tubuh manusia) dan pergerakannya secara 3 dimensi. Ada dua cara yang populer dalam perekaman motion capture, menggunakan Vision Based Camera dan Inertial measurement unit. Inertial Measurement Unit bekerja dengan menggabungkan accelerometer dan gyroscope untuk mendeteksi perubahan sumbu rotasi secara lateral dan angular relative. Perubahan ini yang oleh platform mikro-kontroler Arduino akan diterjemahkan sebagai fungsi gerakan yang nantinya akan direkam sebagai data motion capture. Data dokumentasi digital motion capture yang didapat dari perekaman gerak tari tradisional di Indonesia dapat diaplikasikan untuk banyak hal seperti edukasi, standarisasi, pembuatan animasi, game, dan pelestarian aset budaya. Kata kunci: dokumentasi digital, motion capture, inertia measurement unit, angular relative, pelestarian asset budaya
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Dissertations / Theses on the topic "Inertial data"

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Guner, Dunya Rauf Levent. "Inertial Navigation Sytem Improvement Using Ground Station Data." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615036/index.pdf.

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Missile navigation systems rely on hybrid INS/GPS systems to employ lower grade inertial sensors for the sake of cost and availability. Current inertial navigation systems on missiles can perform accurately for a limited time without GPS aiding. However, GPS is the most likely system that is going to be jammed in a crisis or war by low cost jammers by any opposing force. Missiles do not have adequate equipment to maintain accuracy when GPS is jammed completely in the battle area. In this thesis, a new method is proposed to improve performance of INS systems onboard missiles and autonomous aerial vehicles with EO sensors in a GPS denied environment. Previously laid ground based beacons are used by the missile EO/IIR seeker for bearing-only measurements and position updates are performed by the use of modified artillery survey algorithms based on triangulation techniques which involve angle measurements. For mission planning, two main problems are identified as deployment problem and path planning problem and a tool for the optimal laying of beacons for a given desired trajectory and optimal path planning for a given network of beacons is developed by using evolutionary algorithms and results for test scenarios are discussed.
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Bertani, Federico. "Reconstruction of vehicle dynamics from inertial and GNSS data." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16105/.

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The increasingly massive collection of data from various types of sensors installed on vehicles allows the study and reconstruction of their dynamics in real time, as well as their archiving for subsequent analysis. This Thesis describes the design of a numerical algorithm and its implementation in a program that uses data from inertial and geo-positioning sensors, with applications in industrial contexts and automotive research. The result was made usable through the development of a Python add-on for the Blender graphics program, able to generate a three-dimensional view of the dynamics that can be used by experts and others. Throughout the Thesis, particular attention was paid to the complex nature of the data processed, introducing adequate systems for filtering, interpolation, integration and analysis, aimed at reducing errors and simultaneously optimizing performances.
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Peixoto, João Carlos Pimentel Fidalgo. "Visual and inertial data integration to assist humanoid balance." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17955.

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Mestrado em Engenharia Mecânica
Esta dissertação aborda o problema que consiste na medição do movimento da cabeça de um robot humanóide fundindo dados inerciais e visuais, com o objetivo de obter o output que melhor descreve o movimento da cabeça do humanóide. O seu principal objectivo é perceber e desenvolver um algoritmo usando o Filtro de Kalman, que irá fundir ambas as fontes de dados com o propósito de obter uma nova fonte de informação com um maior grau de confiança. Para cumprir os objectivos, um modelo da cabeça do humanóide, juntamente com as câmaras e os sensores inerciais, vão ser movidos na ponta de um braço robótico industrial, que é usado como grupo de controle (ground truth) no que toca a posição angular. Pontos-chave nos frames obtidos através da câmara, são extra dos e usados para calcular a diferença na posição angular que ocorreu entre frames, que vão mais tarde, juntamente com os dados inerciais obtidos de giroscópios, servir de input a um modelo de um Filtro de Kalman. Uma vez que este dissertação assenta em ferramentas como o Filtro de Kalman, que tem como propósito unir dados de origens diferentes, é essencial que se conheçam os tipos de dados e ferramentas que irão ser utilizados. Assim, várias experiências foram desenvolvidas e estudadas com o intuito de desenvolver o conhecimento nessas matérias. Adicionalmente, erros foram acrescentados aos dados, artificialmente, com o objectivo de emular sensores sensíveis a ruído. No entanto, o sistema continua a ter uma performance positiva.
This thesis addresses the problem of measuring a humanoid robot head motion by merging inertial and visual data, in order to obtain an output that will describe the head motion of the robot. Its primary goal is the understanding and development of an algorithm using the Kalman Filter tool, which will merge inertial and visual data, resulting in a more reliable source of information. To accomplish this, a model of a humanoid robot head, including a camera and inertial sensors, are moved on the tip of an industrial robot's arm which is used as ground truth for angular position. Visual features are extracted from the camera images and used to calculate angular displacement and velocity of the camera, which is then merged with angular velocities from a gyroscope and fed into a Kalman Filter, in order to obtain an output. Since this thesis is expected to merge two di erent kinds of data using the Kalman Filter tool, the need to understand both types of data arises, as well as the way the Kalman Filter operates. Therefore, many experiments were developed and studied with the intent of deepening the knowledge on those matters. The results are quite interesting. Additionally, errors are introduced arti cially into the data to emulate noisy sensors, and the system still performs very well.
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FERRARI, ANNA. "Personalization of Human Activity Recognition Methods using Inertial Data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/305222.

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Recognizing human activities and monitoring population behavior are fun- damental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, lifestyle and behavior tracking are some of the main applications which require the recognition of activities. Activity recognition involves many phases, i.e. the collection, the elaboration and the analysis of information about human activities and behavior. These tasks can be fulfilled manually or automatically, even though a human-based recognition system is not long-term sustainable and scalable. Nevertheless, transforming a human-based recognition system to computer- based automatic system is not a simple task because it requires dedicated hardware and a sophisticated engineering computational and statistical techniques for data preprocessing and analysis. Recently, considerable changes in tech- nologies are largely facilitating this transformation. Indeed, new hardwares and softwares have drastically modified the activity recognition systems. For example, Micro-Electro-Mechanical Systems (MEMS) progress has enabled a reduction in the size of the hardware. Consequently, costs have decreased. Size and cost reduction allows to embed sophisticated sensors into simple devices, such as phones, watches, and even into shoes and clothes, also called wearable devices. Furthermore, low costs, lightness, and small size have made wearable devices’ highly pervasive and accelerated their spread among the population. Today, a very small part of the world population doesn’t own a smartphone. According to Digital 2020: Global Digital Overview, more than 5.19 billion people now use mobile phones. Among the western countries, smartphones and smartwatches are gadgets of people everyday life. The pervasiveness is an undoubted advantage in terms of data generation. Huge amount of data, that is big data, are produced every day. Furthermore, wearable devices together with new advanced software technologies enable data to be sent to servers and instantly analyzed by high performing computers. The availability of big data and new technology improvements, permitted Artificial Intelligence models to rise. In particular, machine learning and deep learning algorithms are predominant in activity recognition. Together with technological and algorithm innovations, the Human Ac- tivity recognition (HAR) research field has born. HAR is a field of research which aims at automatically recognizing people’s physical activities. HAR investigates on the selection of the best hardware, e. g. the best devices to be used for a given application, on the choice of the software to be dedicated to a specific task, and on the increasing of the algorithm performances. HAR has been a very active field of research for years and it is still considered one of the most promising research topic for a large spectrum of ap- plications. In particular, it remains a very challenging research field for many reasons. The selection of devices and sensors, the algorithm’s performances, the collection and the preprocessing of the data, all are requiring further investigation to improve the overall activity recognition system performances. In this work, two main aspects have been investigated: • the benefits of personalization on the algorithm performances, when trained on small size datasets: one of the main issue concerning HAR research community is the lack of the availability of public dataset and labelled data. [...] • a comparison of the performances in HAR obtained both from tradi- tional and personalized machine learning and deep learning techniques.[...]
Recognizing human activities and monitoring population behavior are fun- damental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, lifestyle and behavior tracking are some of the main applications which require the recognition of activities. Activity recognition involves many phases, i.e. the collection, the elaboration and the analysis of information about human activities and behavior. These tasks can be fulfilled manually or automatically, even though a human-based recognition system is not long-term sustainable and scalable. Nevertheless, transforming a human-based recognition system to computer- based automatic system is not a simple task because it requires dedicated hardware and a sophisticated engineering computational and statistical techniques for data preprocessing and analysis. Recently, considerable changes in tech- nologies are largely facilitating this transformation. Indeed, new hardwares and softwares have drastically modified the activity recognition systems. For example, Micro-Electro-Mechanical Systems (MEMS) progress has enabled a reduction in the size of the hardware. Consequently, costs have decreased. Size and cost reduction allows to embed sophisticated sensors into simple devices, such as phones, watches, and even into shoes and clothes, also called wearable devices. Furthermore, low costs, lightness, and small size have made wearable devices’ highly pervasive and accelerated their spread among the population. Today, a very small part of the world population doesn’t own a smartphone. According to Digital 2020: Global Digital Overview, more than 5.19 billion people now use mobile phones. Among the western countries, smartphones and smartwatches are gadgets of people everyday life. The pervasiveness is an undoubted advantage in terms of data generation. Huge amount of data, that is big data, are produced every day. Furthermore, wearable devices together with new advanced software technologies enable data to be sent to servers and instantly analyzed by high performing computers. The availability of big data and new technology improvements, permitted Artificial Intelligence models to rise. In particular, machine learning and deep learning algorithms are predominant in activity recognition. Together with technological and algorithm innovations, the Human Ac- tivity recognition (HAR) research field has born. HAR is a field of research which aims at automatically recognizing people’s physical activities. HAR investigates on the selection of the best hardware, e. g. the best devices to be used for a given application, on the choice of the software to be dedicated to a specific task, and on the increasing of the algorithm performances. HAR has been a very active field of research for years and it is still considered one of the most promising research topic for a large spectrum of ap- plications. In particular, it remains a very challenging research field for many reasons. The selection of devices and sensors, the algorithm’s performances, the collection and the preprocessing of the data, all are requiring further investigation to improve the overall activity recognition system performances. In this work, two main aspects have been investigated: • the benefits of personalization on the algorithm performances, when trained on small size datasets: one of the main issue concerning HAR research community is the lack of the availability of public dataset and labelled data. [...] • a comparison of the performances in HAR obtained both from tradi- tional and personalized machine learning and deep learning techniques.[...]
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Tuul, Viktor. "Online Collaborative Radio-enhanced Visual-inertial SLAM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254952.

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Simultaneous localization and mapping (SLAM) allows robots and other devices to localize and navigate in environments by using a map which itself generates. SLAM for single agent applications has matured and is showing promising results, thus the interest for collaborative SLAM has increased.This thesis proposes a framework for online collaborative radio-enhanced visual-inertial (VI) SLAM where multiple agents can collaborate by having their individually built maps merged and shared amongst each other. The framework is centralized with the aim to allow multiple agents to be managed by a single machine, also rendering it feasible to use the framework with agents that have limited computational resources, e.g. nano drones. Furthermore, radio technology is implemented in the framework which augments the SLAM solution by fusing ultra-wideband (UWB) anchor information into the built maps. This enables agents to query relevant parts of potentially large maps based on their contemporary radio activity.Four individual experiments are conducted to thoroughly evaluate the proposed solution. The results show that the collaborative SLAM system successfully allow agents to localize on parts of a map that other agents have built, running simultaneously. Moreover, the results also show that fusing UWB information into a visual-inertial map allow agents to perform partial-map queries, restricting the search area for visual matches between camera images and the map, reducing the risk of false re-localizations.
Samtidig lokalisering och kartläggning (SLAM) möjliggör för robotar och andra enheter att lokalisera sig och navigera i miljöer genom att nyttja en karta som den själv genererar. SLAM för enskilda agenter har mognat och visar lovande resultat, vilket innebär att intresset för kollaborativ SLAM har ökat.Detta arbete presenterar ett ramverk för kollaborativ radioaugmenterad visuell-inertial (VI) SLAM där flera agenter, exempelvis robotar, kan samverka genom att deras individuellt byggda kartor sammansätts och distributeras mellan varandra. Ramverket är centraliserat i syfte att att flera agenter hanteras av en enda maskin, vilket också möjliggör att ramverket kan användas for agenter med begränsade beräkningsresurser, till exempel nanodrönare. Dessutom så är även radioteknik implementerat i systemet vilket augmenterar SLAM-lösningen genom att inkorporera mottagen information från ultrabandbreddsnoder (UWB) i de byggda kartorna. Detta gör det möjligt för andra agenter att begränsa delar av potentiellt stora kartor for lokalisering baserat på deras innevarande mottagna radiosignaler.Fyra individuella experiment utförs för att grundligt utvärdera den föreslagna lösningen. Resultaten visar att det framtagna ramverket möjliggör för agenter att lokalisera på delar av kartor som andra agenter har byggt, medan de samtidigt körs. Dessutom visar resultaten att implementationen av UWBinformationen i de byggda kartorna medför att agenter kan utföra efterfrågningar på relevanta delar av en global karta. Detta möjliggör begränsningar av sökområdet för visuella träffar mellan kamera och karta och därigenom minska risken för falska omlokaliseringar.
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Bender, Daniel [Verfasser]. "Airborne Navigation by Fusing Inertial and Camera Data / Daniel Bender." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1160594554/34.

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Huai, Jianzhu. "Collaborative SLAM with Crowdsourced Data." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483669256597152.

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Jah, Moriba Kemessia. "Mars aerobraking spacecraft state estimation by processing inertial measurement unit data." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/dissertations/fullcit/3178333.

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Fernandes, Claudio dos Santos. "Visual and inertial data fusion for Globally consistent point cloud registration." Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESSA-9D6GLH.

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This work addresses the problem of mapping 3D static environments by using an RGBD sensor, that captures image and depth, and a MARG sensor, composed by inertial sensors and magnetometers. The approached problem is relevant to the robotics field, since its solution will allow mobile robots to autonomously navigate and map unknown environments. Besides, it has impacts on several applications that perform 3D modeling by using scans obtained from depth sensors. Amongst them, one can mention the digital replication of sculptures and art objects, the modeling of characters for games and movies, and the reconstruction of CAD models from old buildings. We have decided to address the problem by performing a rigid registration of point clouds sequentially captured by the depth sensor, posteriorly using the data provided by the inertial sensor as a guide both during the coarse alignment stage and during the global optimization of the estimated map. During the point cloud alignment based on feature matching, the rotation estimated from the MARG sensor is used as an initial estimation of the attitude between point clouds. Thereby, we seek to match keypoints considering only three translational degrees of freedom. The attitude given by the MARG is also used to reduce the search space for loop closures. The fusion of RGB-D and inertial data is still very little explored in the related literature. A similar work already published only uses inertial data to improve the attitude estimation during the pairwise alignment in an ad-hoc fashion, potentially discarding it under specific conditions, and neglecting the global optimization stage. Since we use a MARG sensor, we assume the sensor drift to be negligible for the purposes of our application, which allows us to always use its data, specially during the global optimization stage. In our experiments, we mapped the walls of a rectangular room with dimensions 9.84m x 7.13m and compared the results with a map from the same scene captured by a Zebedee sensor, state of the art in terms of laser-based 3D mapping. We also compared the proposed algorithm against the RGB-D SLAM methodology, which, unlike our methodology, was not capable of detecting the loop closure region.
Este trabalho aborda o mapeamento tridimensional de ambientes estáticos utilizando um sensor RGB-D, que captura imagem e profundidade, e um sensor MARG, composto de sensores inerciais e magnetômetros. O problema do mapeamento é relevante ao campo da robótica, uma vez que sua solução permitirá a robôs navegarem e mapearem de forma autônoma ambientes desconhecidos. Além disso, traz impactos em diversas aplicações que realizam modelagem 3D a partir de varreduras obtidas de sensores de profundidade. Dentre elas, estão a replicação digital de esculturas e obras de arte, a modelagem de personagens para jogos e filmes, e a obtenção de modelos CAD de edificações antigas. Decidimos abordar o problema realizando o registro rígido de nuvens de pontos adquiridas sequencialmente pelo sensor de profundidade, usando as informações providas pelo sensor inercial como guia tanto no estágio de alinhamento grosseiro quanto na fase de otimização global do mapa gerado. Durante o alinhamento de nuvens de pontos por casamento de features, a rotação estimada pelo sensor MARG é utilizada como uma estimativa inicial da orientação entre nuvens de pontos. Assim, procuramos casar pontos de interesse considerando apenas três graus de liberdade translacionais. A orientação provida pelo MARG também é utilizada para reduzir o espaço de busca por fechamento de loops. A fusão de dados RGB-D com informações inerciais ainda é pouco explorada na literatura. Um trabalho similar já publicado apenas utiliza dados inerciais para melhorar a estimativa da rotação durante o alinhamento par a par de maneira ad-hoc, potencialmente descartando-os em condições específicas, e negligenciando o estágio de otimização global. Por utilizar um sensor MARG, assumimos que o drift do sensor é negligível em nossa aplicação, o que nos permite sempre utilizar seus dados, especialmente durante a fase de otimização global. Em nossos experimentos, realizamos o mapeamento das paretes de um ambiente retangular de dimensões 9,84m x 7,13m e comparamos os resultados com um mapeamento da mesma cena feito a partir de um sensor Zebedee, estado da arte em mapeamento 3D a laser. Também comparamos o algoritmo proposto com a metodologia RGB-D SLAM, que, ao contrário da nossa metodologia, não foi capaz de detectar a região de fechamento de loop.
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Newlin, Michael Linton Hung John Y. Bevly David M. "Design and development of a GPS intermediate frequency and IMU data acquisition system for advanced integrated architectures." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Fall/Theses/NEWLIN_MICHAEL_7.pdf.

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Books on the topic "Inertial data"

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Congming, Cai, and National Institute of Standards and Technology (U.S.), eds. Visualizing terrain and navigation data. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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Sammarco, John J. Mining machine orientation control based on inertial, gravitational, and magnetic sensors. Washington, D.C: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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Wägli, Adrian. Trajectory determination and analysis in sports by satellite and inertial navigation. Zürich: Schweizerische Geodätische Kommission, 2009.

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Strub, Richard. BOREAS level-0 C-130 navigation data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Roseanne, Dominguez, Newcomer J, and Goddard Space Flight Center, eds. BOREAS level-0 C-130 navigation data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Roseanne, Dominguez, Newcomer J, and Goddard Space Flight Center, eds. BOREAS level-0 C-130 navigation data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Rickenbach, Mark Douglas. Correction of inertial navigation system drift errors for an autonomous land vehicle using optical radar terrain data. Monterey, Calif: Naval Postgraduate School, 1987.

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Kenkyūjo, Nagoya Daigaku Purazuma, ed. Workshop report on "simulation techniques for shock wave phenomena" and "characteristics of plasmas in inertial confinement fusion.". Nagoya, Japan: Institute of Plasma Physics, Nagoya University, 1985.

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National Aeronautics and Space Administration (NASA) Staff. Pulsing Inertial Oscillation, Supercell Storms, and Surface Mesonetwork Data. Independently Published, 2018.

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Fortune, Luke. Inertial Propulsion Systems: Scans of Government Archived Data on Advanced Tech. Createspace Independent Publishing Platform, 2012.

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Book chapters on the topic "Inertial data"

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Dingjiang, Luo, and Zhang Baocai. "Physical Data of the Fundamental Stars." In Inertial Coordinate System on the Sky, 430. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0613-6_113.

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Wielen, Roland. "A Comprehensive Astrometric Data Base: An Instrument for Combining Earth-Bound Observations with Hipparcos Data." In Inertial Coordinate System on the Sky, 483–88. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0613-6_131.

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Ha, Quang-Do, and Minh-Triet Tran. "Activity Recognition from Inertial Sensors with Convolutional Neural Networks." In Future Data and Security Engineering, 285–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70004-5_20.

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Wenk, Felix, and Udo Frese. "Pose and Posture Estimation using Inertial Sensor Data." In Formal Modeling and Verification of Cyber-Physical Systems, 308–10. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-09994-7_22.

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Li, Xiang, and Wenbing Liu. "Research on Vector Road Aided Inertial Navigation by Using ICCP Algorithm." In Spatial Data and Intelligence, 87–106. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69873-7_7.

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Li, Xiang, Wenbing Liu, and Qun Chen. "Intelligent Extraction Method of Inertial Navigation Trajectory Behavior Features Considering Road Environment." In Spatial Data and Intelligence, 43–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85462-1_4.

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Alhersh, Taha, Samir Brahim Belhaouari, and Heiner Stuckenschmidt. "Action Recognition Using Local Visual Descriptors and Inertial Data." In Lecture Notes in Computer Science, 123–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34255-5_9.

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Tian, Xiaochun, Xinghang Luo, and Lina Zhang. "A Pedestrian Gait Recognition Method Driven by Inertial Data." In Lecture Notes in Electrical Engineering, 5147–57. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_497.

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Subramanian, M., Y. Harold Robinson, and A. Essakimuthu. "Wi-Fi Based Inertial RSS and Fingerprinting Using Multi-agent Technology." In Internet of Things and Big Data Applications, 231–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39119-5_19.

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Aguilar, Wilbert G., Marco Calderón, Darwin Merizalde, Fabricio Amaguaña, and Jonathan Tituaña. "Visual and Inertial Data-Based Virtual Localization for Urban Combat." In Smart Innovation, Systems and Technologies, 65–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4875-8_6.

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Conference papers on the topic "Inertial data"

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Silva Scarpari, Jose Ricardo, Camila Sardeto Deolindo, Maria Adelia Albano Aratanha, Mauricio Watanabe Ribeiro, Anderson de Souza, Elisa Harumi Kozasa, Daisy Hirata, Jose Elias Matieli, Roberto Gil Annes da Silva, and Carlos Henrique Forster. "Method for the Synchronization of Data Recorders by Coupling Accelerometer Data." In 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2021. http://dx.doi.org/10.1109/inertial51137.2021.9430459.

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Bordoy, Joan, Christian Schindelhauer, Rui Zhang, Fabian Hoflinger, and Leonhard M. Reindl. "Robust Extended Kalman filter for NLOS mitigation and sensor data fusion." In 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2017. http://dx.doi.org/10.1109/isiss.2017.7935670.

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Nastro, Alessandro, Marco Ferrari, Vittorio Ferrari, Camilla I. Mura, Andrea Labombarda, Marco Viti, and Sandro Dalle Feste. "Noise Reduction by Data Fusion in a Multisensor System of Replicated MEMS Inclinometers." In 2022 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2022. http://dx.doi.org/10.1109/inertial53425.2022.9787531.

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Masino, Johannes, Matthias Luh, Michael Frey, and Frank Gauterin. "Inertial sensor for an autonomous data acquisition of a novel automotive acoustic measurement system." In 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2017. http://dx.doi.org/10.1109/isiss.2017.7935649.

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Demrozi, Florenc, Marin Jereghi, and Graziano Pravadelli. "Towards the automatic data annotation for human activity recognition based on wearables and BLE beacons." In 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2021. http://dx.doi.org/10.1109/inertial51137.2021.9430457.

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Renaudin, Valerie, Yacouba Kone, Hanyuan Fu, and Ni Zhu. ""Physics" vs "Brain": Challenge of labeling wearable inertial data for step detection for Artificial Intelligence." In 2022 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2022. http://dx.doi.org/10.1109/inertial53425.2022.9787763.

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Adams, Benjamin, Calum Macrae, Mani Entezami, Kevin Ridley, Archie Kubba, Yu-Hung Lien, Sachin Kinge, and Kai Bongs. "The development of a High data rate atom interferometric gravimeter (HIDRAG) for gravity map matching navigation." In 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2021. http://dx.doi.org/10.1109/inertial51137.2021.9430461.

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Felix, Dauer, Daniel Gorges, and Andreas Wienss. "Impacts of Inertial Sensor Errors on both Data Fusion and Attitude-Based Bicycle Rider Assistance Systems in order to derive Sensor Requirements." In 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 2019. http://dx.doi.org/10.1109/isiss.2019.8739291.

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Luca, Ramona, Silviu-Ioan Bejinariu, Hariton Costin, Florin Rotaru, and Gladiola Petroiu. "Human Activity Recognition using Inertial Data." In 2021 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE). IEEE, 2021. http://dx.doi.org/10.1109/atee52255.2021.9425112.

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Klein, I. "Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation." In 2022 DGON Inertial Sensors and Systems (ISS). IEEE, 2022. http://dx.doi.org/10.1109/iss55898.2022.9926294.

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Reports on the topic "Inertial data"

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Haak, Jeffrey W. Verification of Robustified Kalman Filters for the Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) Data,. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada288609.

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Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41440.

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A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
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Dhillon, Nathan, Andrew Hannay, and Robin Workman. Next Generation Monitoring Systems. TRL, July 2022. http://dx.doi.org/10.58446/npwb2214.

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Survey vehicles, operating at traffic-speed, are deployed across the road network to assess the condition of road pavements. These apply high-quality (and high cost) equipment to measure condition. However, significant progress has been made in the development of low-cost sensors and data collection units that may have potential for application in highways. This project has aimed to understand the capabilities of this emerging technology. The project explores the technologies and combines a Raspberry-Pi based Data Acquisition System, compact camera, GPS, inertial measurement system, Wifi and 4G GSM comms and a low-cost Solid State LiDAR into a prototype device. The total cost is a few hundred pounds. Trials characterise the prototype system. Although the solid-state LiDAR sensors are not found to be robust in this application, the remaining sensors show strong potential for use in road condition assessment. A wider trial of the prototype system in a potential application – the measurement of roughness (IRI) on developing world road networks – was carried out in El Salvador. The prototype shows comparable performance with alternatives, combined with higher levels of practicality and capability, and the potential for higher levels of consistency through a common low-cost measurement platform. In the light of this research, it is felt that, following refinements to the prototype, the initial application for the device would be for condition surveys in developing world nations.
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Habib, Ayman, Darcy M. Bullock, Yi-Chun Lin, Raja Manish, and Radhika Ravi. Field Test Bed for Evaluating Embedded Vehicle Sensors with Indiana Companies. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317385.

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With the advent of modern sensing technology, mapping products have begun to achieve an unprecedented precision of measurement. Considering their diverse use cases, several factors play a role in what would make the resulting measurements accurate. For light detection and ranging (LiDAR) and photogrammetry-based mapping solutions that implement vehicles outfitted with laser ranging devices, RGB cameras, and global navigation satellite system/inertial navigation system (GNSS/INS) georeferencing units, the quality of the derived mapping products is governed by the combined accuracy of the various sensors. While ranging errors associated with LiDAR systems or the imaging quality of RGB cameras are sensor-dependent and are mostly constant, the accuracy of a georeferencing unit depends on a variety of extrinsic factors, including but not limited to, availability of clear line-of-path to GNSS satellites and presence of radio interferences. The quality of the GNSS signal, in turn, is affected by the grade of hardware components used and, to a great extent, obstructions to signal reception. This document reports some of the major challenges of vehicle-based mobile mapping with regards to GNSS/INS navigation. The background of GNSS/INS positioning is discussed to build a framework for trajectory enhancement as well as improvement of LiDAR mapping products. The focus is put on using available sensor data from LiDAR and/or cameras to enhance their position/orientation quality. Some best practices in light of potential trajectory deterioration are also recommended.
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Wu, Yingjie, Selim Gunay, and Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ytgv8834.

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Bridges often serve as key links in local and national transportation networks. Bridge closures can result in severe costs, not only in the form of repair or replacement, but also in the form of economic losses related to medium- and long-term interruption of businesses and disruption to surrounding communities. In addition, continuous functionality of bridges is very important after any seismic event for emergency response and recovery purposes. Considering the importance of these structures, the associated structural design philosophy is shifting from collapse prevention to maintaining functionality in the aftermath of moderate to strong earthquakes, referred to as “resiliency” in earthquake engineering research. Moreover, the associated construction philosophy is being modernized with the utilization of accelerated bridge construction (ABC) techniques, which strive to reduce the impact of construction on traffic, society, economy and on-site safety. This report presents two bridge systems that target the aforementioned issues. A study that combined numerical and experimental research was undertaken to characterize the seismic performance of these bridge systems. The first part of the study focuses on the structural system-level response of highway bridges that incorporate a class of innovative connecting devices called the “V-connector,”, which can be used to connect two components in a structural system, e.g., the column and the bridge deck, or the column and its foundation. This device, designed by ACII, Inc., results in an isolation surface at the connection plane via a connector rod placed in a V-shaped tube that is embedded into the concrete. Energy dissipation is provided by friction between a special washer located around the V-shaped tube and a top plate. Because of the period elongation due to the isolation layer and the limited amount of force transferred by the relatively flexible connector rod, bridge columns are protected from experiencing damage, thus leading to improved seismic behavior. The V-connector system also facilitates the ABC by allowing on-site assembly of prefabricated structural parts including those of the V-connector. A single-column, two-span highway bridge located in Northern California was used for the proof-of-concept of the proposed V-connector protective system. The V-connector was designed to result in an elastic bridge response based on nonlinear dynamic analyses of the bridge model with the V-connector. Accordingly, a one-third scale V-connector was fabricated based on a set of selected design parameters. A quasi-static cyclic test was first conducted to characterize the force-displacement relationship of the V-connector, followed by a hybrid simulation (HS) test in the longitudinal direction of the bridge to verify the intended linear elastic response of the bridge system. In the HS test, all bridge components were analytically modeled except for the V-connector, which was simulated as the experimental substructure in a specially designed and constructed test setup. Linear elastic bridge response was confirmed according to the HS results. The response of the bridge with the V-connector was compared against that of the as-built bridge without the V-connector, which experienced significant column damage. These results justified the effectiveness of this innovative device. The second part of the study presents the HS test conducted on a one-third scale two-column bridge bent with self-centering columns (broadly defined as “resilient columns” in this study) to reduce (or ultimately eliminate) any residual drifts. The comparison of the HS test with a previously conducted shaking table test on an identical bridge bent is one of the highlights of this study. The concept of resiliency was incorporated in the design of the bridge bent columns characterized by a well-balanced combination of self-centering, rocking, and energy-dissipating mechanisms. This combination is expected to lead to minimum damage and low levels of residual drifts. The ABC is achieved by utilizing precast columns and end members (cap beam and foundation) through an innovative socket connection. In order to conduct the HS test, a new hybrid simulation system (HSS) was developed, utilizing commonly available software and hardware components in most structural laboratories including: a computational platform using Matlab/Simulink [MathWorks 2015], an interface hardware/software platform dSPACE [2017], and MTS controllers and data acquisition (DAQ) system for the utilized actuators and sensors. Proper operation of the HSS was verified using a trial run without the test specimen before the actual HS test. In the conducted HS test, the two-column bridge bent was simulated as the experimental substructure while modeling the horizontal and vertical inertia masses and corresponding mass proportional damping in the computer. The same ground motions from the shaking table test, consisting of one horizontal component and the vertical component, were applied as input excitations to the equations of motion in the HS. Good matching was obtained between the shaking table and the HS test results, demonstrating the appropriateness of the defined governing equations of motion and the employed damping model, in addition to the reliability of the developed HSS with minimum simulation errors. The small residual drifts and the minimum level of structural damage at large peak drift levels demonstrated the superior seismic response of the innovative design of the bridge bent with self-centering columns. The reliability of the developed HS approach motivated performing a follow-up HS study focusing on the transverse direction of the bridge, where the entire two-span bridge deck and its abutments represented the computational substructure, while the two-column bridge bent was the physical substructure. This investigation was effective in shedding light on the system-level performance of the entire bridge system that incorporated innovative bridge bent design beyond what can be achieved via shaking table tests, which are usually limited by large-scale bridge system testing capacities.
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Coastal Lidar And Radar Imaging System (CLARIS) mobile terrestrial lidar survey along the Outer Banks, North Carolina in Currituck and Dare counties. Coastal and Hydraulics Laboratory (U.S.), January 2020. http://dx.doi.org/10.21079/11681/39419.

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The Coastal Observation and Analysis Branch (COAB) located at the Field Research Facility (FRF) conducts quarterly surveys and post-storm surveys along up to 60 kilometers of coastline within the vicinity of the FRF to assess, evaluate, and provide updated observations of the morphology of the foreshore and dune system. The surveys are conducted using a mobile terrestrial LiDAR scanner coupled with an Inertial Navigation System (INS). Traditionally the surveys coincide with a low tide, exposing the widest swath of visible sediment to the scanner as well as enough wind-sea swell or texture to induce wave breaking upon the interior sandbars. The wave field is measured with X-Band radar which records a spatial time series of wave direction and speed. Data for the survey region was collected using the VZ-2000's mobile, 3D scanning mode where the scanner continuously rotates the line scan 360 degrees as the vehicle progresses forward. Elevation measurements are acquired on all sides of the vehicle except for the topography directly underneath the vehicle. As the vehicle moves forward, the next rotation will capture the previous position's occluded data area. Laser data is acquired in mobile 3D radar mode with a pulse repetition rate of 300kHz, theta resolution of 0.19 degrees and phi resolution of 0.625 degrees. Horizontal Datum NAD83(2011), Projection North Carolina State Plane (3200) meters; Vertical Datum NAVD88, meters with geoid09 applied.
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Coastal Lidar And Radar Imaging System (CLARIS) mobile terrestrial lidar survey along the Outer Banks, North Carolina in Currituck and Dare counties. Coastal and Hydraulics Laboratory (U.S.), January 2020. http://dx.doi.org/10.21079/11681/39419.

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The Coastal Observation and Analysis Branch (COAB) located at the Field Research Facility (FRF) conducts quarterly surveys and post-storm surveys along up to 60 kilometers of coastline within the vicinity of the FRF to assess, evaluate, and provide updated observations of the morphology of the foreshore and dune system. The surveys are conducted using a mobile terrestrial LiDAR scanner coupled with an Inertial Navigation System (INS). Traditionally the surveys coincide with a low tide, exposing the widest swath of visible sediment to the scanner as well as enough wind-sea swell or texture to induce wave breaking upon the interior sandbars. The wave field is measured with X-Band radar which records a spatial time series of wave direction and speed. Data for the survey region was collected using the VZ-2000's mobile, 3D scanning mode where the scanner continuously rotates the line scan 360 degrees as the vehicle progresses forward. Elevation measurements are acquired on all sides of the vehicle except for the topography directly underneath the vehicle. As the vehicle moves forward, the next rotation will capture the previous position's occluded data area. Laser data is acquired in mobile 3D radar mode with a pulse repetition rate of 300kHz, theta resolution of 0.19 degrees and phi resolution of 0.625 degrees. Horizontal Datum NAD83(2011), Projection North Carolina State Plane (3200) meters; Vertical Datum NAVD88, meters with geoid09 applied.
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