Academic literature on the topic 'Inertial data'
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Journal articles on the topic "Inertial data"
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.
Full textSun, 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.
Full textWang, 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.
Full textUshaq, 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.
Full textGao, 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.
Full textGildeh, 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.
Full textSvensson, 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.
Full textZhang, 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.
Full textMolinari, 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.
Full textNugroho, 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.
Full textDissertations / Theses on the topic "Inertial data"
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.
Full textBertani, 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/.
Full textPeixoto, 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.
Full textEsta 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.
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.
Full textRecognizing 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.[...]
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.
Full textSamtidig 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.
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.
Full textHuai, Jianzhu. "Collaborative SLAM with Crowdsourced Data." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483669256597152.
Full textJah, 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.
Full textFernandes, 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.
Full textEste 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.
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.
Full textBooks on the topic "Inertial data"
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.
Find full textSammarco, 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.
Find full textWägli, Adrian. Trajectory determination and analysis in sports by satellite and inertial navigation. Zürich: Schweizerische Geodätische Kommission, 2009.
Find full textStrub, Richard. BOREAS level-0 C-130 navigation data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.
Find full textRoseanne, 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.
Find full textRoseanne, 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.
Find full textRickenbach, 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.
Find full textKenkyūjo, 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.
Find full textNational Aeronautics and Space Administration (NASA) Staff. Pulsing Inertial Oscillation, Supercell Storms, and Surface Mesonetwork Data. Independently Published, 2018.
Find full textFortune, Luke. Inertial Propulsion Systems: Scans of Government Archived Data on Advanced Tech. Createspace Independent Publishing Platform, 2012.
Find full textBook chapters on the topic "Inertial data"
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.
Full textWielen, 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.
Full textHa, 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.
Full textWenk, 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.
Full textLi, 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.
Full textLi, 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.
Full textAlhersh, 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.
Full textTian, 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.
Full textSubramanian, 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.
Full textAguilar, 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.
Full textConference papers on the topic "Inertial data"
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.
Full textBordoy, 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.
Full textNastro, 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.
Full textMasino, 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.
Full textDemrozi, 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.
Full textRenaudin, 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.
Full textAdams, 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.
Full textFelix, 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.
Full textLuca, 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.
Full textKlein, 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.
Full textReports on the topic "Inertial data"
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.
Full textBrodie, 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.
Full textDhillon, Nathan, Andrew Hannay, and Robin Workman. Next Generation Monitoring Systems. TRL, July 2022. http://dx.doi.org/10.58446/npwb2214.
Full textHabib, 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.
Full textWu, 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.
Full textCoastal 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.
Full textCoastal 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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