Littérature scientifique sur le sujet « SLAM mapping »
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Articles de revues sur le sujet "SLAM mapping"
Saat, Shahrizal, AN MF Airini, Muhammad Salihin Saealal, A. R. Wan Norhisyam et M. S. Farees Ezwan. « Hector SLAM 2D Mapping for Simultaneous Localization and Mapping (SLAM) ». Journal of Engineering and Applied Sciences 14, no 16 (10 novembre 2019) : 5610–15. http://dx.doi.org/10.36478/jeasci.2019.5610.5615.
Texte intégralLu, Xiaoyun, Hu Wang, Shuming Tang, Huimin Huang et Chuang Li. « DM-SLAM : Monocular SLAM in Dynamic Environments ». Applied Sciences 10, no 12 (21 juin 2020) : 4252. http://dx.doi.org/10.3390/app10124252.
Texte intégralBoyu, Kuang, Chen Yuheng et Rana Zeeshan A. « OG-SLAM : A real-time and high-accurate monocular visual SLAM framework ». Trends in Computer Science and Information Technology 7, no 2 (26 juillet 2022) : 047–54. http://dx.doi.org/10.17352/tcsit.000050.
Texte intégralPeng, Tao, Dingnan Zhang, Don Lahiru Nirmal Hettiarachchi et John Loomis. « An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms ». Electronic Imaging 2020, no 6 (26 janvier 2020) : 325–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-074.
Texte intégralSun, Liuxin, Junyu Wei, Shaojing Su et Peng Wu. « SOLO-SLAM : A Parallel Semantic SLAM Algorithm for Dynamic Scenes ». Sensors 22, no 18 (15 septembre 2022) : 6977. http://dx.doi.org/10.3390/s22186977.
Texte intégralSkrzypczyński, Piotr. « Simultaneous localization and mapping : A feature-based probabilistic approach ». International Journal of Applied Mathematics and Computer Science 19, no 4 (1 décembre 2009) : 575–88. http://dx.doi.org/10.2478/v10006-009-0045-z.
Texte intégralSong, Jooeun, et Joongjin Kook. « Visual SLAM Based Spatial Recognition and Visualization Method for Mobile AR Systems ». Applied System Innovation 5, no 1 (5 janvier 2022) : 11. http://dx.doi.org/10.3390/asi5010011.
Texte intégralZhang, Haoyang. « Deep Learning Applications in Simultaneous Localization and Mapping ». Journal of Physics : Conference Series 2181, no 1 (1 janvier 2022) : 012012. http://dx.doi.org/10.1088/1742-6596/2181/1/012012.
Texte intégralZhang, Zijie, et Jing Zeng. « A Survey on Visual Simultaneously Localization and Mapping ». Frontiers in Computing and Intelligent Systems 1, no 1 (2 août 2022) : 18–21. http://dx.doi.org/10.54097/fcis.v1i1.1089.
Texte intégralLuo, Kaiqing, Manling Lin, Pengcheng Wang, Siwei Zhou, Dan Yin et Haolan Zhang. « Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment ». Mathematical Problems in Engineering 2020 (23 septembre 2020) : 1–13. http://dx.doi.org/10.1155/2020/4724310.
Texte intégralThèses sur le sujet "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.
Texte intégralCarlson, Justin. « Mapping Large, Urban Environments with GPS-Aided SLAM ». Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/44.
Texte intégralTouchette, Sébastien. « Recovering Cholesky Factor in Smoothing and Mapping ». Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37935.
Texte intégralMaddern, 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.
Texte intégralÜzer, Ferit. « Hybrid mapping for large urban environments ». Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22675/document.
Texte intégralIn 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.
Texte intégralPereira, 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.
Texte intégralDoctor 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.
Texte intégralPascoe, Geoffrey. « Robust lifelong visual navigation and mapping ». Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:c0bfa5fb-fa0a-48ed-8d26-90fa167ef6cd.
Texte intégralPretto, Alberto. « Visual-SLAM for Humanoid Robots ». Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426516.
Texte intégralNell’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.
Livres sur le sujet "SLAM mapping"
Valencia, Rafael, et 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.
Texte intégralMullane, John, Ba-Ngu Vo, Martin Adams et 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.
Texte intégralMullane, John. Random Finite Sets for Robot Mapping and SLAM : New Concepts in Autonomous Robotic Map Representations. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
Trouver le texte intégralAmen, Alan E. Soil Landscape Analysis Project (SLAP) methods in soil surveys. a Springfield, VA : Denver, CO, 1987.
Trouver le texte intégralAmen, Alan E. Soil Landscape Analysis Project (SLAP) methods in soil surveys. Denver, CO : U.S. Dept. of the Interior, Bureau of Land Management, 1987.
Trouver le texte intégralAndrade-Cetto, Juan, et Rafael Valencia. Mapping, Planning and Exploration with Pose SLAM. Springer, 2018.
Trouver le texte intégralAndrade-Cetto, Juan, et Rafael Valencia. Mapping, Planning and Exploration with Pose SLAM. Springer, 2017.
Trouver le texte intégralBurguera, Antoni, et Francisco Bonin-Font, dir. Localization, Mapping and SLAM in Marine and Underwater Environments. MDPI, 2022. http://dx.doi.org/10.3390/books978-3-0365-5498-3.
Texte intégralVo, Ba-Ngu, Martin David Adams et John Stephen Mullane. Random Finite Sets for Robot Mapping & SLAM : New Concepts in Autonomous Robotic Map Representations. Springer, 2011.
Trouver le texte intégralVo, Ba-Ngu, Martin David Adams, John Stephen Mullane et Ba-Tuong Vo. Random Finite Sets for Robot Mapping & SLAM : New Concepts in Autonomous Robotic Map Representations. Springer, 2013.
Trouver le texte intégralChapitres de livres sur le sujet "SLAM mapping"
Berns, Karsten, et Ewald von Puttkamer. « Simultaneous localization and mapping (SLAM) ». Dans Autonomous Land Vehicles, 146–72. Wiesbaden : Vieweg+Teubner, 2009. http://dx.doi.org/10.1007/978-3-8348-9334-5_6.
Texte intégralPerera, Samunda, Dr Nick Barnes et Dr Alexander Zelinsky. « Exploration : Simultaneous Localization and Mapping (SLAM) ». Dans Computer Vision, 268–75. Boston, MA : Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_280.
Texte intégralPerera, Samunda, Nick Barnes et Alexander Zelinsky. « Exploration : Simultaneous Localization and Mapping (SLAM) ». Dans Computer Vision, 412–20. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_280.
Texte intégralEllery, Alex. « Autonomous Navigation—Self-localization and Mapping (SLAM) ». Dans Planetary Rovers, 331–74. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-03259-2_9.
Texte intégralJoukhadar, Abdulkader, Dalia Kass Hanna, Andreas Müller et Christoph Stöger. « UKF-Assisted SLAM for 4WDDMR Localization and Mapping ». Dans 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.
Texte intégralChatterjee, Amitava, Anjan Rakshit et N. Nirmal Singh. « Simultaneous Localization and Mapping (SLAM) in Mobile Robots ». Dans 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.
Texte intégralKung, Da-Wei, Chen-Chien Hsu, Wei-Yen Wang et Jacky Baltes. « Adaptive Computation Algorithm for Simultaneous Localization and Mapping (SLAM) ». Dans 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.
Texte intégralFernández-Moral, Eduardo, Vicente Arévalo et Javier González-Jiménez. « Hybrid Metric-topological Mapping for Large Scale Monocular SLAM ». Dans 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.
Texte intégralBrooks, Alex, et Tim Bailey. « HybridSLAM : Combining FastSLAM and EKF-SLAM for Reliable Mapping ». Dans 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.
Texte intégralZhang, He, Zifeng Hou, Nanjun Li et Shuang Song. « A Graph-Based Hierarchical SLAM Framework for Large-Scale Mapping ». Dans Intelligent Robotics and Applications, 439–48. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_44.
Texte intégralActes de conférences sur le sujet "SLAM mapping"
Aguilar-Gonzalez, Abiel, et Miguel Arias-Estrada. « Dense mapping for monocular-SLAM ». Dans 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2016. http://dx.doi.org/10.1109/ipin.2016.7743671.
Texte intégralYang, Zhuoyue, et Dianxi Shi. « Mapping Technology in Visual SLAM ». Dans the 2018 2nd International Conference. New York, New York, USA : ACM Press, 2018. http://dx.doi.org/10.1145/3297156.3297163.
Texte intégralXue, Wuyang, Rendong Ying, Zheng Gong, Ruihang Miao, Fei Wen et Peilin Liu. « SLAM Based Topological Mapping and Navigation ». Dans 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2020. http://dx.doi.org/10.1109/plans46316.2020.9110190.
Texte intégralLi, Ping, et Zhongming Ke. « Feature-based SLAM for Dense Mapping ». Dans 2019 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2019. http://dx.doi.org/10.1109/icamechs.2019.8861671.
Texte intégralTong, Chi Hay, Timothy D. Barfoot et Erick Dupuis. « 3D SLAM for planetary worksite mapping ». Dans 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048242.
Texte intégralChi Hay Tong, T. D. Barfoot et E. Dupuis. « 3D SLAM for planetary worksite mapping ». Dans 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6094577.
Texte intégralChoudhary, Siddharth, Alexander J. B. Trevor, Henrik I. Christensen et Frank Dellaert. « SLAM with object discovery, modeling and mapping ». Dans 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6942683.
Texte intégralKhairuddin, Alif Ridzuan, Mohamad Shukor Talib et Habibollah Haron. « Review on simultaneous localization and mapping (SLAM) ». Dans 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2015. http://dx.doi.org/10.1109/iccsce.2015.7482163.
Texte intégralChan, Teng Hooi, Henrik Hesse et Song Guang Ho. « LiDAR-Based 3D SLAM for Indoor Mapping ». Dans 2021 7th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2021. http://dx.doi.org/10.1109/iccar52225.2021.9463503.
Texte intégralChen, Yuwei, Changhui Jiang, Lingli Zhu, Harri Kaartinen, Juha Hyyppa, Jian Tan, Hannu Hyyppa, Hui Zhou, Ruizhi Chen et Ling Pei. « SLAM Based Indoor Mapping Comparison:Mobile or Terrestrial ? » Dans 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS). IEEE, 2018. http://dx.doi.org/10.1109/upinlbs.2018.8559707.
Texte intégralRapports d'organisations sur le sujet "SLAM mapping"
Kelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA : Defense Technical Information Center, avril 2006. http://dx.doi.org/10.21236/ad1016045.
Texte intégralKelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA : Defense Technical Information Center, avril 2006. http://dx.doi.org/10.21236/ada636872.
Texte intégralChristie, Benjamin, Osama Ennasr et Garry Glaspell. Autonomous navigation and mapping in a simulated environment. Engineer Research and Development Center (U.S.), septembre 2021. http://dx.doi.org/10.21079/11681/42006.
Texte intégralChristie, Benjamin, Osama Ennasr et Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), novembre 2021. http://dx.doi.org/10.21079/11681/42385.
Texte intégralLee, W. S., Victor Alchanatis et Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, janvier 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
Texte intégralMaksud, A. K. M., Khandaker Reaz Hossain et 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, mai 2022. http://dx.doi.org/10.19088/clarissa.2022.002.
Texte intégral