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Статті в журналах з теми "Graph-based localization and mapping"
Xiong, Hui, Youping Chen, Xiaoping Li, and Bing Chen. "A two-level optimized graph-based simultaneous localization and mapping algorithm." Industrial Robot: An International Journal 45, no. 6 (October 15, 2018): 758–65. http://dx.doi.org/10.1108/ir-04-2018-0078.
Повний текст джерелаXu, Hao, Huafei Sun, Yongqiang Cheng, and Hao Liu. "Wireless sensor networks localization based on graph embedding with polynomial mapping." Computer Networks 106 (September 2016): 151–60. http://dx.doi.org/10.1016/j.comnet.2016.06.032.
Повний текст джерелаZhu, Zihan, Yi Zhang, Weijun Wang, Wei Feng, Haowen Luo, and Yaojie Zhang. "Adaptive Adjustment of Factor’s Weight for a Multi-Sensor SLAM." Journal of Physics: Conference Series 2451, no. 1 (March 1, 2023): 012004. http://dx.doi.org/10.1088/1742-6596/2451/1/012004.
Повний текст джерелаMukherjee, Shohin, Michael Kaess, Joseph N. Martel, and Cameron N. Riviere. "EyeSAM: graph-based localization and mapping of retinal vasculature during intraocular microsurgery." International Journal of Computer Assisted Radiology and Surgery 14, no. 5 (February 21, 2019): 819–28. http://dx.doi.org/10.1007/s11548-019-01925-1.
Повний текст джерелаRen, Zhuli, Liguan Wang, and Lin Bi. "Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment." Sensors 19, no. 13 (July 1, 2019): 2915. http://dx.doi.org/10.3390/s19132915.
Повний текст джерелаDai, Kai, Bohua Sun, Guanpu Wu, Shuai Zhao, Fangwu Ma, Yufei Zhang, and Jian Wu. "LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios." Journal of Imaging 9, no. 2 (February 20, 2023): 52. http://dx.doi.org/10.3390/jimaging9020052.
Повний текст джерелаZhang Tianxi, 张天喜, 周军 Zhou Jun, 廖华丽 Liao Huali, and 杨跟 Yang Gen. "Simultaneous Localization and Mapping Strategy of Graph Optimization Based on Three-Dimensional Laser." Laser & Optoelectronics Progress 56, no. 20 (2019): 201502. http://dx.doi.org/10.3788/lop56.201502.
Повний текст джерелаWu, Xinzhao, Peiqing Li, Qipeng Li, and Zhuoran Li. "Two-dimensional-simultaneous Localisation and Mapping Study Based on Factor Graph Elimination Optimisation." Sustainability 15, no. 2 (January 8, 2023): 1172. http://dx.doi.org/10.3390/su15021172.
Повний текст джерелаXu, Shaoyan, Tao Wang, Congyan Lang, Songhe Feng, and Yi Jin. "Graph-based visual odometry for VSLAM." Industrial Robot: An International Journal 45, no. 5 (August 20, 2018): 679–87. http://dx.doi.org/10.1108/ir-04-2018-0061.
Повний текст джерелаOKADA, Nobuya, Daichi ABE, Satoshi SUZUKI, Kojiro IIZUKA, and Takashi KAWAMURA. "2A2-R04 Image and Shape features combined Landmarks based Graph SLAM(Localization and Mapping)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _2A2—R04_1—_2A2—R04_4. http://dx.doi.org/10.1299/jsmermd.2014._2a2-r04_1.
Повний текст джерелаДисертації з теми "Graph-based localization and mapping"
Agarwal, Pratik [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Robust graph-based localization and mapping = Robuste Graph-basierte Lokalisierung und Kartierung." Freiburg : Universität, 2015. http://d-nb.info/111980549X/34.
Повний текст джерелаSünderhauf, Niko. "Robust optimization for simultaneous localization and mapping." Thesis, Technischen Universitat Chemnitz, 2012. https://eprints.qut.edu.au/109667/1/109667.pdf.
Повний текст джерелаSünderhauf, Niko. "Robust Optimization for Simultaneous Localization and Mapping." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-86443.
Повний текст джерелаJama, Michal. "Monocular vision based localization and mapping." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8561.
Повний текст джерелаDepartment of Electrical and Computer Engineering
Balasubramaniam Natarajan
Dale E. Schinstock
In this dissertation, two applications related to vision-based localization and mapping are considered: (1) improving navigation system based satellite location estimates by using on-board camera images, and (2) deriving position information from video stream and using it to aid an auto-pilot of an unmanned aerial vehicle (UAV). In the first part of this dissertation, a method for analyzing a minimization process called bundle adjustment (BA) used in stereo imagery based 3D terrain reconstruction to refine estimates of camera poses (positions and orientations) is presented. In particular, imagery obtained with pushbroom cameras is of interest. This work proposes a method to identify cases in which BA does not work as intended, i.e., the cases in which the pose estimates returned by the BA are not more accurate than estimates provided by a satellite navigation systems due to the existence of degrees of freedom (DOF) in BA. Use of inaccurate pose estimates causes warping and scaling effects in the reconstructed terrain and prevents the terrain from being used in scientific analysis. Main contributions of this part of work include: 1) formulation of a method for detecting DOF in the BA; and 2) identifying that two camera geometries commonly used to obtain stereo imagery have DOF. Also, this part presents results demonstrating that avoidance of the DOF can give significant accuracy gains in aerial imagery. The second part of this dissertation proposes a vision based system for UAV navigation. This is a monocular vision based simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video-stream from a single camera. This is different from common SLAM solutions that use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. The SLAM solution was built by significantly modifying and extending a recent open-source SLAM solution that is fundamentally different from a traditional approach to solving SLAM problem. The modifications made are those needed to provide the position measurements necessary for the navigation solution on a UAV while simultaneously building the map, all while maintaining control of the UAV. The main contributions of this part include: 1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; 2) improved performance of the SLAM algorithm for lower camera frame rates; and 3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible, and can be effective in Global Positioning System denied environments.
Cummins, Mark. "Probabilistic localization and mapping in appearance space." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:a34370f2-a2a9-40b5-9a2d-1c8c616ff07a.
Повний текст джерелаLim, Yu-Xi. "Efficient wireless location estimation through simultaneous localization and mapping." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28219.
Повний текст джерелаCommittee Chair: Owen, Henry; Committee Member: Copeland, John; Committee Member: Giffin, Jonathon; Committee Member: Howard, Ayanna; Committee Member: Riley, George.
Schaefer, Alexander [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Highly accurate lidar-based mapping and localization for mobile robots." Freiburg : Universität, 2020. http://d-nb.info/1207756016/34.
Повний текст джерелаOliveira, Douglas Coelho Braga de. "Dynamic-object-aware simultaneous localization and mapping for augmented reality applications." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/8059.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Realidade Aumentada (RA) é uma tecnologia que permite combinar objetos virtuais tridimensionais com um ambiente predominantemente real, de forma a construir um novo ambiente onde os objetos reais e virtuais podem interagir uns com os outros em tempo real. Para fazer isso, é necessário encontrar a pose do observador (câmera, HMD, óculos inteligentes, etc.) em relação a um sistema de coordenadas global. Geralmente, algum objeto físico conhecido é usado para marcar o referencial para as projeções e para a posição do observador. O problema de Localização e Mapeamento Simultâneo (SLAM) se origina da comunidade de robótica como uma condição necessária para se construir robôs verdadeiramente autônomos, capazes de se auto localizarem em um ambiente desconhecido ao mesmo tempo que constroem um mapa da cena observada a partir de informações capturadas por um conjunto de sensores. A principal contribuição do SLAM para a RA é permitir aplicações em ambientes despreparados, ou seja, sem marcadores. No entanto, ao eliminar o marcador, perdemos o referencial para a projeção dos objetos virtuais e a principal fonte de interação entre os elementos reais e virtuais. Embora o mapa gerado possa ser processado a fim de encontrar uma estrutura conhecida, como um plano predominante, para usá-la como referencial, isso ainda não resolve a questão das interações. Na literatura recente, encontramos trabalhos que integram um sistema de reconhecimento de objetos ao SLAM e incorporam tais objetos ao mapa. Frequentemente, assume-se um mapa estático, devido às limitações das técnicas envolvidas, de modo que o objeto é usado apenas para fornecer informações semânticas sobre a cena. Neste trabalho, propomos um novo framework que permite estimar simultaneamente a posição da câmera e de objetos para cada quadro de vídeo em tempo real. Dessa forma, cada objeto é independente e pode se mover pelo mapa livremente, assim como nos métodos baseados em marcadores, mas mantendo as vantagens que o SLAM fornece. Implementamos a estrutura proposta sobre um sistema SLAM de última geração a fim de validar nossa proposta e demonstrar a potencial aplicação em Realidade Aumentada.
Augmented Reality (AR) is a technology that allows combining three-dimensional virtual objects with an environment predominantly real in a way to build a new environment where both real and virtual objects can interact with each other in real-time. To do this, it is required to nd the pose of the observer (camera, HMD, smart glasses etc) in relation to a global coordinate system. Commonly, some well known physical object, called marker, is used to de ne the referential for both virtual objects and the observer's position. The Simultaneous Localization and Mapping (SLAM) problem borns from robotics community as a way to build truly autonomous robots by allowing they to localize themselves while they build a map of the observed scene from the input data of their coupled sensors. SLAM-based Augmented Reality is an active and evolving research line. The main contribution of the SLAM to the AR is to allow applications on unprepared environments, i.e., without markers. However, by eliminating the marker object, we lose the referential for virtual object projection and the main source of interaction between real and virtual elements. Although the generated map can be processed in order to nd a known structure, e.g. a predominant plane, to use it as the referential system, this still not solve for interactions. In the recent literature, we can found works that integrate an object recognition system to the SLAM in a way the objects are incorporated into the map. The SLAM map is frequently assumed to be static, due to limitations on techniques involved, so that on these works the object is just used to provide semantic information about the scene. In this work, we propose a new framework that allows estimating simultaneously the camera and object positioning for each camera image in real time. In this way, each object is independent and can move through the map as well as in the marker-based methods but with the SLAM advantages kept. We develop our proposed framework over a stateof- the-art SLAM system in order to evaluate our proposal and demonstrate potentials application in Augmented Reality.
Lee, Chun-Fan Computer Science & Engineering Faculty of Engineering UNSW. "Towards topological mapping with vision-based simultaneous localization and map building." Awarded by:University of New South Wales. Computer Science & Engineering, 2008. http://handle.unsw.edu.au/1959.4/41551.
Повний текст джерелаDroeschel, David Marcel [Verfasser]. "Efficient Methods for Lidar-based Mapping and Localization / David Marcel Droeschel." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/122166929X/34.
Повний текст джерелаКниги з теми "Graph-based localization and mapping"
Dyer, Paul S., Carol A. Munro, and Rosie E. Bradshaw. Fungal genetics. Edited by Christopher C. Kibbler, Richard Barton, Neil A. R. Gow, Susan Howell, Donna M. MacCallum, and Rohini J. Manuel. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198755388.003.0005.
Повний текст джерелаChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаStatistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Знайти повний текст джерелаPractical R for biologists: an introduction. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0000.
Повний текст джерелаЧастини книг з теми "Graph-based localization and mapping"
Werner, Martin. "Simultaneous Localization and Mapping in Buildings." In Indoor Location-Based Services, 181–201. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10699-1_8.
Повний текст джерелаWallgrün, Jan Oliver. "Voronoi Graph Matching for Robot Localization and Mapping." In Transactions on Computational Science IX, 76–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16007-3_4.
Повний текст джерелаZhu, Xiaorui, Youngshik Kim, Mark Andrew Minor, and Chunxin Qiu. "Terrain-Inclination–Based Localization and Mapping." In Autonomous Mobile Robots in Unknown Outdoor Environments, 187–204. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2017. |: CRC Press, 2017. http://dx.doi.org/10.1201/9781315151496-9.
Повний текст джерелаZhou, Mu, Qiao Zhang, Zengshan Tian, Feng Qiu, and Qing Jiang. "WLAN Localization Without Location Fingerprinting Using Logic Graph Mapping." In Lecture Notes in Electrical Engineering, 545–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_56.
Повний текст джерелаChatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Simultaneous Localization and Mapping (SLAM) in Mobile Robots." In 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.
Повний текст джерелаTsintotas, Konstantinos A., Loukas Bampis, and Antonios Gasteratos. "The Revisiting Problem in Simultaneous Localization and Mapping." In Online Appearance-Based Place Recognition and Mapping, 1–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09396-8_1.
Повний текст джерелаLiu, Jiayi, Randy C. Hoover, and Jeff S. McGough. "Mobile Fiducial-Based Collaborative Localization and Mapping (CLAM)." In Proceedings of the 2020 USCToMM Symposium on Mechanical Systems and Robotics, 196–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43929-3_18.
Повний текст джерелаGarcia-Fidalgo, Emilio, and Alberto Ortiz. "Probabilistic Appearance-Based Mapping and Localization Using Visual Features." In Pattern Recognition and Image Analysis, 277–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_33.
Повний текст джерелаWeikersdorfer, David, Raoul Hoffmann, and Jörg Conradt. "Simultaneous Localization and Mapping for Event-Based Vision Systems." In Lecture Notes in Computer Science, 133–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39402-7_14.
Повний текст джерелаBryson, Mitch, and Salah Sukkarieh. "Inertial Sensor-Based Simultaneous Localization and Mapping for UAVs." In Handbook of Unmanned Aerial Vehicles, 401–31. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-90-481-9707-1_5.
Повний текст джерелаТези доповідей конференцій з теми "Graph-based localization and mapping"
Carlone, Luca, Rosario Aragues, Jose Castellanos, and Basilio Bona. "A Linear Approximation for Graph-based Simultaneous Localization and Mapping." In Robotics: Science and Systems 2011. Robotics: Science and Systems Foundation, 2011. http://dx.doi.org/10.15607/rss.2011.vii.006.
Повний текст джерелаLeitinger, Erik, Florian Meyer, Fredrik Tufvesson, and Klaus Witrisal. "Factor graph based simultaneous localization and mapping using multipath channel information." In 2017 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2017. http://dx.doi.org/10.1109/iccw.2017.7962732.
Повний текст джерелаYin, Jingchun, Luca Carlone, Stefano Rosa, and Basilio Bona. "Graph-based robust localization and mapping for autonomous mobile robotic navigation." In 2014 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2014. http://dx.doi.org/10.1109/icma.2014.6885953.
Повний текст джерелаBeinschob, Patric, and Christoph Reinke. "Graph SLAM based mapping for AGV localization in large-scale warehouses." In 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2015. http://dx.doi.org/10.1109/iccp.2015.7312637.
Повний текст джерелаMaddern, Will, Michael Milford, and Gordon Wyeth. "Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph." In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012). IEEE, 2012. http://dx.doi.org/10.1109/iros.2012.6386186.
Повний текст джерелаZhou, Mu, Qiao Zhang, Zengshan Tian, Kunjie Xu, Feng Qiu, and Qi Wu. "Graph drawing based WLAN indoor mapping and localization using signal correlation via edge detection." In 2015 IEEE International Wireless Symposium (IWS). IEEE, 2015. http://dx.doi.org/10.1109/ieee-iws.2015.7164524.
Повний текст джерелаBabu, Benzun P. Wisely, David Cyganski, and James Duckworth. "Gyroscope assisted scalable visual simultaneous localization and mapping." In 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS). IEEE, 2014. http://dx.doi.org/10.1109/upinlbs.2014.7033731.
Повний текст джерелаZheng, Junyuan, Yuan He, and Masaaki Kondo. "Exploiting Data Parallelism in Graph-Based Simultaneous Localization and Mapping: A Case Study with GPU Accelerations." In HPC ASIA 2023: International Conference on High Performance Computing in Asia-Pacific Region. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578178.3578237.
Повний текст джерелаDanping, Jia, Duan Guangxue, Wang Nan, Zhou Zhigang, Zhong Zhenyu, and Lei Huan. "Simultaneous Localization and Mapping based on Lidar." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833308.
Повний текст джерелаMeghdari, A., K. Kobravi, H. Safyallah, M. Moeeni, Y. Khatami, and H. Khasteh. "A New Approach to Sonar Based Indoor Mapping Localization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85269.
Повний текст джерелаЗвіти організацій з теми "Graph-based localization and mapping"
Christie, Benjamin, Osama Ennasr, and Garry Glaspell. Autonomous navigation and mapping in a simulated environment. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42006.
Повний текст джерелаLee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
Повний текст джерелаWan, Wei. A New Approach to the Decomposition of Incompletely Specified Functions Based on Graph Coloring and Local Transformation and Its Application to FPGA Mapping. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6582.
Повний текст джерелаBennett, Alan B., Arthur A. Schaffer, Ilan Levin, Marina Petreikov, and Adi Doron-Faigenboim. Manipulating fruit chloroplasts as a strategy to improve fruit quality. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598148.bard.
Повний текст джерела