Literatura académica sobre el tema "SLAM mapping"
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Artículos de revistas sobre el tema "SLAM mapping"
Saat, Shahrizal, AN MF Airini, Muhammad Salihin Saealal, A. R. Wan Norhisyam y M. S. Farees Ezwan. "Hector SLAM 2D Mapping for Simultaneous Localization and Mapping (SLAM)". Journal of Engineering and Applied Sciences 14, n.º 16 (10 de noviembre de 2019): 5610–15. http://dx.doi.org/10.36478/jeasci.2019.5610.5615.
Texto completoLu, Xiaoyun, Hu Wang, Shuming Tang, Huimin Huang y Chuang Li. "DM-SLAM: Monocular SLAM in Dynamic Environments". Applied Sciences 10, n.º 12 (21 de junio de 2020): 4252. http://dx.doi.org/10.3390/app10124252.
Texto completoBoyu, Kuang, Chen Yuheng y Rana Zeeshan A. "OG-SLAM: A real-time and high-accurate monocular visual SLAM framework". Trends in Computer Science and Information Technology 7, n.º 2 (26 de julio de 2022): 047–54. http://dx.doi.org/10.17352/tcsit.000050.
Texto completoPeng, Tao, Dingnan Zhang, Don Lahiru Nirmal Hettiarachchi y John Loomis. "An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms". Electronic Imaging 2020, n.º 6 (26 de enero de 2020): 325–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-074.
Texto completoSun, Liuxin, Junyu Wei, Shaojing Su y Peng Wu. "SOLO-SLAM: A Parallel Semantic SLAM Algorithm for Dynamic Scenes". Sensors 22, n.º 18 (15 de septiembre de 2022): 6977. http://dx.doi.org/10.3390/s22186977.
Texto completoSkrzypczyński, Piotr. "Simultaneous localization and mapping: A feature-based probabilistic approach". International Journal of Applied Mathematics and Computer Science 19, n.º 4 (1 de diciembre de 2009): 575–88. http://dx.doi.org/10.2478/v10006-009-0045-z.
Texto completoSong, Jooeun y Joongjin Kook. "Visual SLAM Based Spatial Recognition and Visualization Method for Mobile AR Systems". Applied System Innovation 5, n.º 1 (5 de enero de 2022): 11. http://dx.doi.org/10.3390/asi5010011.
Texto completoZhang, Haoyang. "Deep Learning Applications in Simultaneous Localization and Mapping". Journal of Physics: Conference Series 2181, n.º 1 (1 de enero de 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2181/1/012012.
Texto completoZhang, Zijie y Jing Zeng. "A Survey on Visual Simultaneously Localization and Mapping". Frontiers in Computing and Intelligent Systems 1, n.º 1 (2 de agosto de 2022): 18–21. http://dx.doi.org/10.54097/fcis.v1i1.1089.
Texto completoLuo, Kaiqing, Manling Lin, Pengcheng Wang, Siwei Zhou, Dan Yin y Haolan Zhang. "Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment". Mathematical Problems in Engineering 2020 (23 de septiembre de 2020): 1–13. http://dx.doi.org/10.1155/2020/4724310.
Texto completoTesis sobre el tema "SLAM mapping"
Valencia, Carreño Rafael. "Mapping, planning and exploration with Pose SLAM". Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/117471.
Texto completoCarlson, Justin. "Mapping Large, Urban Environments with GPS-Aided SLAM". Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/44.
Texto completoTouchette, Sébastien. "Recovering Cholesky Factor in Smoothing and Mapping". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37935.
Texto completoMaddern, William Paul. "Continuous appearance-based localisation and mapping". Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65841/2/William_Maddern_Thesis.pdf.
Texto completoÜzer, Ferit. "Hybrid mapping for large urban environments". Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22675/document.
Texto completoIn this thesis, a novel vision based hybrid mapping framework which exploits metric, topological and semantic information is presented. We aim to obtain better computational efficiency than pure metrical mapping techniques, better accuracy as well as usability for robot guidance compared to the topological mapping. A crucial step of any mapping system is the loop closure detection which is the ability of knowing if the robot is revisiting a previously mapped area. Therefore, we first propose a hierarchical loop closure detection framework which also constructs the global topological structure of our hybrid map. Using this loop closure detection module, a hybrid mapping framework is proposed in two step. The first step can be understood as a topo-metric map with nodes corresponding to certain regions in the environment. Each node in turn is made up of a set of images acquired in that region. These maps are further augmented with metric information at those nodes which correspond to image sub-sequences acquired while the robot is revisiting the previously mapped area. The second step augments this model by using road semantics. A Conditional Random Field based classification on the metric reconstruction is used to semantically label the local robot path (road in our case) as straight, curved or junctions. Metric information of regions with curved roads and junctions is retained while that of other regions is discarded in the final map. Loop closure is performed only on junctions thereby increasing the efficiency and also accuracy of the map. By incorporating all of these new algorithms, the hybrid framework presented can perform as a robust, scalable SLAM approach, or act as a main part of a navigation tool which could be used on a mobile robot or an autonomous car in outdoor urban environments. Experimental results obtained on public datasets acquired in challenging urban environments are provided to demonstrate our approach
Frost, Duncan. "Long range monocular SLAM". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:af38cfa6-fc0a-48ab-b919-63c440ae8774.
Texto completoPereira, Savio Joseph. "On the utilization of Simultaneous Localization and Mapping(SLAM) along with vehicle dynamics in Mobile Road Mapping Systems". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/94425.
Texto completoDoctor of Philosophy
Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The objective of this research work is to improve the accuracy of MRMS by investigating methods to improve the sensor data fusion process. The main focus of this work is to apply the principles from the field of Simultaneous Localization and Mapping (SLAM) in order to improve the accuracy of MRMS. The concept of SLAM has been successfully applied to the field of mobile robot navigation and thus the motivation of this work is to investigate its application to the problem of mobile road mapping. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence this work also investigates whether knowledge regarding the dynamics of the system can be used to improve the accuracy. Also developed as part of this work is a novel method for identifying outliers in road surface datasets and estimating elevations at road surface grid nodes. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the-art methods.
Carranza, Jose Martinez. "Efficient monocular SLAM by using a structure-driven mapping". Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574263.
Texto completoPascoe, Geoffrey. "Robust lifelong visual navigation and mapping". Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:c0bfa5fb-fa0a-48ed-8d26-90fa167ef6cd.
Texto completoPretto, Alberto. "Visual-SLAM for Humanoid Robots". Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426516.
Texto completoNell’ambito della robotica, il Simultaneous Localization and Mapping (SLAM) é il processo grazie al quale un robot autonomo é in grado di creare una mappa dell’ambiente circostante e allo stesso tempo di localizzarsi avvalendosi di tale mappa. Negli ultimi anni un considerevole numero di ricercatori ha sviluppato nuove famiglie di algoritmi di SLAM, basati su vari sensori e utilizzando varie piattaforme robotiche. Uno degli ambiti più complessi nella ricerca sullo SLAM é il cosiddetto Visual-SLAM, che prevede l’utilizzo di vari tipi di telecamera come sensore per la navigazione. Le telecamere sono sensori economici che raccolgono molte informazioni sull’ambiente circostante. D’altro canto, la complessità degli algoritmi di visione artificiale e la forte dipendenza degli approcci attualmente realizzati dalle caratteristiche dell’ambiente, rendono il Visual-SLAM un problema lontano dal poter essere considerato risolto. Molti degli algoritmi di SLAM sono solitamente testati usando robot dotati di ruote. Sebbene tali piattaforme siano ormai robuste e stabili, la ricerca sulla progettazione di nuove piattaforme robotiche sta in parte migrando verso la robotica umanoide. Proprio come gli esseri umani, i robot umanoidi sono in grado di adattarsi ai cambiamenti dell’ambiente per raggiungere efficacemente i propri obiettivi. Nonostante ciò, solo pochi ricercatori hanno focalizzato i loro sforzi su implementazioni stabili di algoritmi di SLAM e Visual-SLAM adatti ai robot umanoidi. Tali piattaforme robotiche introducono nuove problematiche che possono compromettere la stabilità degli algoritmi di navigazione convenzionali, specie se basati sulla visione. I robot umanoidi sono dotati di un alto grado di libertà di movimento, con la possibilità di effettuare velocemente movimenti complessi: tali caratteristiche introducono negli spostamenti vibrazioni non deterministiche in grado di compromettere l’affidabilit` dei dati sensoriali acquisiti, per esempio introducendo nei flussi video effetti indesiderati quali il motion blur. A causa dei vincoli imposti dal bilanciamento del corpo, inoltre, tali robot non sempre possono essere dotati di unit` di elaborazione molto performanti che spesso sono ingombranti e dal peso elevato: ci` limita l’utilizzo di algoritmi complessi e computazionalmente gravosi. Infine, al contrario di quanto accade per i robot dotati di ruote, la complessa cinematica di un robot umanoide impedisce di ricostruire il movimento basandosi sulle informazioni provenienti dagli encoder posti sui motori. In questa tesi ci si é focalizzati sullo studio e sullo sviluppo di nuove metodologie per affrontare il problema del Visual-SLAM, ponendo particolare enfasi ai problemi legati all’utilizzo di piccoli robot umanoidi dotati di una singola telecamera come piattaforme per gli esperimenti. I maggiori sforzi nell’ambito della ricerca sullo SLAM e sul Visual-SLAM si sono concentrati nel campo del processo di stima dello stato del robot, ad esempio la stima della propria posizione e della mappa dell’ambiente. D’altra parte, la maggior parte delle problematiche incontrate nella ricerca sul Visual-SLAM sono legate al processo di percezione, ovvero all’interpretazione dei dati provenienti dai sensori. In questa tesi ci si é perciò concentrati sul miglioramento dei processi percettivi da un punto di vista della visione artificiale. Sono stati affrontati i problemi che scaturiscono dall’utilizzo di piccoli robot umanoidi come piattaforme sperimentali, come ad esempio la bassa capacità di calcolo, la bassa qualit` dei dati sensoriali e l’elevato numero di gradi di libertà nei movimenti. La bassa capacità di calcolo ha portato alla creazione di un nuovo metodo per misurare la similarità tra le immagini, che fa uso di una descrizione dell’immagine compatta, utilizzabile in applicazioni di SLAM topologico. Il problema del motion blur é stato affrontato proponendo una nuova tecnica di rilevamento di feature visive, unitamente ad un nuovo schema di tracking, robusto an- che in caso di motion blur non uniforme. E’ stato altresì sviluppato un framework per l’odometria basata sulle immagini, che fa uso delle feature visive presentate. Si propone infine un approccio al Visual-SLAM basato sulle omografie, che sfrutta le informazioni ottenute da una singola telecamera montata su un robot umanoide. Tale approccio si basa sull’assunzione che il robot si muove su una superficie piana. Tutti i metodi proposti sono stati validati con esperimenti e studi comparativi, usando sia dataset standard che immagini acquisite dalle telecamere installate su piccoli robot umanoidi.
Libros sobre el tema "SLAM mapping"
Valencia, Rafael y Juan Andrade-Cetto. Mapping, Planning and Exploration with Pose SLAM. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60603-3.
Texto completoMullane, John, Ba-Ngu Vo, Martin Adams y Ba-Tuong Vo. Random Finite Sets for Robot Mapping and SLAM. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21390-8.
Texto completoMullane, John. Random Finite Sets for Robot Mapping and SLAM: New Concepts in Autonomous Robotic Map Representations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Buscar texto completoAmen, Alan E. Soil Landscape Analysis Project (SLAP) methods in soil surveys. a Springfield, VA: Denver, CO, 1987.
Buscar texto completoAmen, Alan E. Soil Landscape Analysis Project (SLAP) methods in soil surveys. Denver, CO: U.S. Dept. of the Interior, Bureau of Land Management, 1987.
Buscar texto completoAndrade-Cetto, Juan y Rafael Valencia. Mapping, Planning and Exploration with Pose SLAM. Springer, 2018.
Buscar texto completoAndrade-Cetto, Juan y Rafael Valencia. Mapping, Planning and Exploration with Pose SLAM. Springer, 2017.
Buscar texto completoBurguera, Antoni y Francisco Bonin-Font, eds. Localization, Mapping and SLAM in Marine and Underwater Environments. MDPI, 2022. http://dx.doi.org/10.3390/books978-3-0365-5498-3.
Texto completoVo, Ba-Ngu, Martin David Adams y John Stephen Mullane. Random Finite Sets for Robot Mapping & SLAM: New Concepts in Autonomous Robotic Map Representations. Springer, 2011.
Buscar texto completoVo, Ba-Ngu, Martin David Adams, John Stephen Mullane y Ba-Tuong Vo. Random Finite Sets for Robot Mapping & SLAM: New Concepts in Autonomous Robotic Map Representations. Springer, 2013.
Buscar texto completoCapítulos de libros sobre el tema "SLAM mapping"
Berns, Karsten y Ewald von Puttkamer. "Simultaneous localization and mapping (SLAM)". En Autonomous Land Vehicles, 146–72. Wiesbaden: Vieweg+Teubner, 2009. http://dx.doi.org/10.1007/978-3-8348-9334-5_6.
Texto completoPerera, Samunda, Dr Nick Barnes y Dr Alexander Zelinsky. "Exploration: Simultaneous Localization and Mapping (SLAM)". En Computer Vision, 268–75. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_280.
Texto completoPerera, Samunda, Nick Barnes y Alexander Zelinsky. "Exploration: Simultaneous Localization and Mapping (SLAM)". En Computer Vision, 412–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_280.
Texto completoEllery, Alex. "Autonomous Navigation—Self-localization and Mapping (SLAM)". En Planetary Rovers, 331–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-03259-2_9.
Texto completoJoukhadar, Abdulkader, Dalia Kass Hanna, Andreas Müller y Christoph Stöger. "UKF-Assisted SLAM for 4WDDMR Localization and Mapping". En Mechanism, Machine, Robotics and Mechatronics Sciences, 259–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89911-4_19.
Texto completoChatterjee, Amitava, Anjan Rakshit y N. Nirmal Singh. "Simultaneous Localization and Mapping (SLAM) in Mobile Robots". En Vision Based Autonomous Robot Navigation, 167–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_7.
Texto completoKung, Da-Wei, Chen-Chien Hsu, Wei-Yen Wang y Jacky Baltes. "Adaptive Computation Algorithm for Simultaneous Localization and Mapping (SLAM)". En Advances in Intelligent Systems and Computing, 75–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_7.
Texto completoFernández-Moral, Eduardo, Vicente Arévalo y Javier González-Jiménez. "Hybrid Metric-topological Mapping for Large Scale Monocular SLAM". En Informatics in Control, Automation and Robotics, 217–32. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10891-9_12.
Texto completoBrooks, Alex y Tim Bailey. "HybridSLAM: Combining FastSLAM and EKF-SLAM for Reliable Mapping". En Springer Tracts in Advanced Robotics, 647–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00312-7_40.
Texto completoZhang, He, Zifeng Hou, Nanjun Li y Shuang Song. "A Graph-Based Hierarchical SLAM Framework for Large-Scale Mapping". En Intelligent Robotics and Applications, 439–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_44.
Texto completoActas de conferencias sobre el tema "SLAM mapping"
Aguilar-Gonzalez, Abiel y Miguel Arias-Estrada. "Dense mapping for monocular-SLAM". En 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2016. http://dx.doi.org/10.1109/ipin.2016.7743671.
Texto completoYang, Zhuoyue y Dianxi Shi. "Mapping Technology in Visual SLAM". En the 2018 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3297156.3297163.
Texto completoXue, Wuyang, Rendong Ying, Zheng Gong, Ruihang Miao, Fei Wen y Peilin Liu. "SLAM Based Topological Mapping and Navigation". En 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2020. http://dx.doi.org/10.1109/plans46316.2020.9110190.
Texto completoLi, Ping y Zhongming Ke. "Feature-based SLAM for Dense Mapping". En 2019 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2019. http://dx.doi.org/10.1109/icamechs.2019.8861671.
Texto completoTong, Chi Hay, Timothy D. Barfoot y Erick Dupuis. "3D SLAM for planetary worksite mapping". En 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048242.
Texto completoChi Hay Tong, T. D. Barfoot y E. Dupuis. "3D SLAM for planetary worksite mapping". En 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6094577.
Texto completoChoudhary, Siddharth, Alexander J. B. Trevor, Henrik I. Christensen y Frank Dellaert. "SLAM with object discovery, modeling and mapping". En 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6942683.
Texto completoKhairuddin, Alif Ridzuan, Mohamad Shukor Talib y Habibollah Haron. "Review on simultaneous localization and mapping (SLAM)". En 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2015. http://dx.doi.org/10.1109/iccsce.2015.7482163.
Texto completoChan, Teng Hooi, Henrik Hesse y Song Guang Ho. "LiDAR-Based 3D SLAM for Indoor Mapping". En 2021 7th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2021. http://dx.doi.org/10.1109/iccar52225.2021.9463503.
Texto completoChen, Yuwei, Changhui Jiang, Lingli Zhu, Harri Kaartinen, Juha Hyyppa, Jian Tan, Hannu Hyyppa, Hui Zhou, Ruizhi Chen y Ling Pei. "SLAM Based Indoor Mapping Comparison:Mobile or Terrestrial ?" En 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS). IEEE, 2018. http://dx.doi.org/10.1109/upinlbs.2018.8559707.
Texto completoInformes sobre el tema "SLAM mapping"
Kelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA: Defense Technical Information Center, abril de 2006. http://dx.doi.org/10.21236/ad1016045.
Texto completoKelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA: Defense Technical Information Center, abril de 2006. http://dx.doi.org/10.21236/ada636872.
Texto completoChristie, Benjamin, Osama Ennasr y Garry Glaspell. Autonomous navigation and mapping in a simulated environment. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/42006.
Texto completoChristie, Benjamin, Osama Ennasr y Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), noviembre de 2021. http://dx.doi.org/10.21079/11681/42385.
Texto completoLee, W. S., Victor Alchanatis y Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, enero de 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
Texto completoMaksud, A. K. M., Khandaker Reaz Hossain y Amit Arulanantham. Mapping of Slums and Identifying Children Engaged in Worst Forms of Child Labour Living in Slums and Working in Neighbourhood Areas. Institute of Development Studies, mayo de 2022. http://dx.doi.org/10.19088/clarissa.2022.002.
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