Academic literature on the topic 'Semantic SLAM'
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Journal articles on the topic "Semantic SLAM"
Sun, Liuxin, Junyu Wei, Shaojing Su, and Peng Wu. "SOLO-SLAM: A Parallel Semantic SLAM Algorithm for Dynamic Scenes." Sensors 22, no. 18 (September 15, 2022): 6977. http://dx.doi.org/10.3390/s22186977.
Full textYou, Yingxuan, Peng Wei, Jialun Cai, Weibo Huang, Risheng Kang, and Hong Liu. "MISD-SLAM: Multimodal Semantic SLAM for Dynamic Environments." Wireless Communications and Mobile Computing 2022 (April 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/7600669.
Full textBowman, Sean, Kostas Daniilidis, and George Pappas. "Robust Object-Level Semantic Visual SLAM Using Semantic Keypoints." Field Robotics 2, no. 1 (March 10, 2022): 513–24. http://dx.doi.org/10.55417/fr.2022018.
Full textGuan, Peiyu, Zhiqiang Cao, Erkui Chen, Shuang Liang, Min Tan, and Junzhi Yu. "A real-time semantic visual SLAM approach with points and objects." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988142090544. http://dx.doi.org/10.1177/1729881420905443.
Full textHan, Shuangquan, and Zhihong Xi. "Dynamic Scene Semantics SLAM Based on Semantic Segmentation." IEEE Access 8 (2020): 43563–70. http://dx.doi.org/10.1109/access.2020.2977684.
Full textLong, Fei, Lei Ding, and Jianfeng Li. "DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow." Biomimetics 7, no. 4 (October 13, 2022): 163. http://dx.doi.org/10.3390/biomimetics7040163.
Full textJia, Shifeng. "LRD-SLAM: A Lightweight Robust Dynamic SLAM Method by Semantic Segmentation Network." Wireless Communications and Mobile Computing 2022 (November 21, 2022): 1–19. http://dx.doi.org/10.1155/2022/7332390.
Full textFan, Yingchun, Qichi Zhang, Yuliang Tang, Shaofen Liu, and Hong Han. "Blitz-SLAM: A semantic SLAM in dynamic environments." Pattern Recognition 121 (January 2022): 108225. http://dx.doi.org/10.1016/j.patcog.2021.108225.
Full textMiao, Sheng, Xiaoxiong Liu, Dazheng Wei, and Changze Li. "A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information." ISPRS International Journal of Geo-Information 10, no. 10 (October 4, 2021): 673. http://dx.doi.org/10.3390/ijgi10100673.
Full textWu, Yakun, Li Luo, Shujuan Yin, Mengqi Yu, Fei Qiao, Hongzhi Huang, Xuesong Shi, Qi Wei, and Xinjun Liu. "An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment." Applied Sciences 11, no. 4 (February 18, 2021): 1828. http://dx.doi.org/10.3390/app11041828.
Full textDissertations / Theses on the topic "Semantic SLAM"
Salas-Moreno, Renato F. "Dense semantic SLAM." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24524.
Full textBaxter, David P. Nav E. (David Paul)Massachusetts Institute of Technology. "Toward robust active semantic SLAM via Max-Mixtures." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127041.
Full textThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 75-78).
In a step towards the level of autonomy seen in humans, this work attempts to emulate a high level and low level approach to world representation and short term adaptation. Specifically, this work demonstrates an implementation of robotic perception that transforms stereo camera and LIDAR sensor data into a sparse map of semantic objects and a locally consistent flexible occupancy grid. This provides a topological representation for grouping objects into higher level classes and a geometric map for traditional planning. Additionally, a reactive dynamic window obstacle avoidance system is shown to quickly plan short term trajectories that avoid both static and dynamic objects while progressing towards a goal. To combine computational efficiency with the robust advantages of multimodal inference, this work uses Semantic Max Mixture factors to approximate multimodal belief in a manner compatible to nonlinear least squares solvers. Experimental results are presented using a RACECAR mobile robot operating in several hallways of MIT, using AprilTags as surrogates for objects in the Semantic Max Mixtures Algorithm. Future work will seek to further integrate the components to create a closed-loop active semantic navigation and mapping algorithm.
by David P. Baxter.
Nav. E.
S.M.
Nav.E. Massachusetts Institute of Technology, Department of Mechanical Engineering
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
Ghorpade, Vijaya Kumar. "3D Semantic SLAM of Indoor Environment with Single Depth Sensor." Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC085/document.
Full textIntelligent autonomous actions in an ordinary environment by a mobile robot require maps. A map holds the spatial information about the environment and gives the 3D geometry of the surrounding of the robot to not only avoid collision with complex obstacles, but also selflocalization and for task planning. However, in the future, service and personal robots will prevail and need arises for the robot to interact with the environment in addition to localize and navigate. This interaction demands the next generation robots to understand, interpret its environment and perform tasks in human-centric form. A simple map of the environment is far from being sufficient for the robots to co-exist and assist humans in the future. Human beings effortlessly make map and interact with environment, and it is trivial task for them. However, for robots these frivolous tasks are complex conundrums. Layering the semantic information on regular geometric maps is the leap that helps an ordinary mobile robot to be a more intelligent autonomous system. A semantic map augments a general map with the information about entities, i.e., objects, functionalities, or events, that are located in the space. The inclusion of semantics in the map enhances the robot’s spatial knowledge representation and improves its performance in managing complex tasks and human interaction. Many approaches have been proposed to address the semantic SLAM problem with laser scanners and RGB-D time-of-flight sensors, but it is still in its nascent phase. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Time-of-flight cameras have dramatically changed the field of range imaging, and surpassed the traditional scanners in terms of rapid acquisition of data, simplicity and price. And it is believed that these depth sensors will be ubiquitous in future robotic applications. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Starting with a brief motivation in the first chapter for semantic stance in normal maps, the state-of-the-art methods are discussed in the second chapter. Before using the camera for data acquisition, the noise characteristics of it has been studied meticulously, and properly calibrated. The novel noise filtering algorithm developed in the process, helps to get clean data for better scan matching and SLAM. The quality of the SLAM process is evaluated using a context-based similarity score metric, which has been specifically designed for the type of acquisition parameters and the data which have been used. Abstracting semantic layer on the reconstructed point cloud from SLAM has been done in two stages. In large-scale higher-level semantic interpretation, the prominent surfaces in the indoor environment are extracted and recognized, they include surfaces like walls, door, ceiling, clutter. However, in indoor single scene object-level semantic interpretation, a single 2.5D scene from the camera is parsed and the objects, surfaces are recognized. The object recognition is achieved using a novel shape signature based on probability distribution of 3D keypoints that are most stable and repeatable. The classification of prominent surfaces and single scene semantic interpretation is done using supervised machine learning and deep learning systems. To this end, the object dataset and SLAM data are also made publicly available for academic research
Zingoni, Jacopo. "Semantic Enrichment of Scientific Documents with Semantic Lenses – Developing methodologies, tools and prototypes for their concrete use." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4476/.
Full textRogers, John Gilbert. "Life-long mapping of objects and places in domestic environments." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47736.
Full textTrevor, Alexander J. B. "Semantic mapping for service robots: building and using maps for mobile manipulators in semi-structured environments." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53583.
Full textSalehi, Achkan. "Localisation précise d'un véhicule par couplage vision/capteurs embarqués/systèmes d'informations géographiques." Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC064/document.
Full textThe fusion between sensors and databases whose errors are independant is the most re-liable and therefore most widespread solution to the localization problem. Current autonomousand semi-autonomous vehicles, as well as augmented reality applications targeting industrialcontexts exploit large sensor and database graphs that are difficult and expensive to synchro-nize and calibrate. Thus, the democratization of these technologies requires the exploration ofthe possiblity of exploiting low-cost and easily accessible sensors and databases. These infor-mation sources are naturally tainted by higher uncertainty levels, and many obstacles to theireffective and efficient practical usage persist. Moreover, the recent but dazzling successes ofdeep neural networks in various tasks seem to indicate that they could be a viable and low-costalternative to some components of current SLAM systems.In this thesis, we focused on large-scale localization of a vehicle in a georeferenced co-ordinate frame from a low-cost system, which is based on the fusion between a monocularvideo stream, 3d non-textured but georeferenced building models, terrain elevation models anddata either from a low-cost GPS or from vehicle odometry. Our work targets the resolutionof two problems. The first one is related to the fusion via barrier term optimization of VS-LAM and positioning measurements provided by a low-cost GPS. This method is, to the bestof our knowledge, the most robust against GPS uncertainties, but it is more demanding in termsof computational resources. We propose an algorithmic optimization of that approach basedon the definition of a novel barrier term. The second problem is the data association problembetween the primitives that represent the geometry of the scene (e.g. 3d points) and the 3d buil-ding models. Previous works in that area use simple geometric criteria and are therefore verysensitive to occlusions in urban environments. We exploit deep convolutional neural networksin order to identify and associate elements from the map that correspond to 3d building mo-del façades. Although our contributions are for the most part independant from the underlyingSLAM system, we based our experiments on constrained key-frame based bundle adjustment.The solutions that we propose are evaluated on synthetic sequences as well as on real urbandatasets. These experiments show important performance gains for VSLAM/GPS fusion, andconsiderable improvements in the robustness of building constraints to occlusions
Drouilly, Romain. "Cartographie hybride métrique topologique et sémantique pour la navigation dans de grands environnements." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4037/document.
Full textUtonomous navigation is one of the most challenging tasks for mobile robots. It requires the ability to localize itself or a target and to find the best path linking both positions avoiding obstacles. Towards this goal, robots build a map of the environment that models its geometry or topology. However building such a map in large scale environments is challenging due to the large amount of data to manage and localization could become intractable. Additionally, an ever changing environment leads to fast obsolescence of the map that becomes useless. As shown in this thesis, introducing semantics in those maps dramatically improves navigation performances of robots in realistic environments. Scene parsing allows to build extremely compact semantic models of the scene that are used for fast relocalization using a graph-matching approach. They are powerful tools to understand scene and they are used to extend the map beyond perceptual limits of the robot through reasoning. Statistical analysis of those models is used to build an embryo of common sens which allows to detect labeling errors and to update the map using algorithms designed to maintain a stable model of the world despite occlusions due to dynamic objects. Finally semantics is used to select the best route to a target position according to high level criteria instead of metrical constraints, allowing intelligent navigation
Fakhfakh, Inès. "Semantic based cloud broker architecture optimizing users satisfaction." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0008/document.
Full textCloud Computing is a dynamic new technology that has huge potentials in enterprises and markets. The dynamicity and the increasing complexity of Cloud architectures involve several management challenges. In this work, we are interested in Service Level Agreement (SLA) management. Actually, there is no standard to express Cloud SLA, so, providers describe their SLAs in different manner and different languages, which leaves the user puzzled about the choice of its Cloud provider. To overcome these problems, we introduce a Cloud Broker Architecture managing the SLA between providers and consumers. It aims to assist users in establishing and negotiating SLA contracts and to help them in finding the best provider that satisfies their service level expectations. Our broker SLA contracts are formalized as OWL ontologies as they allow hiding the heterogeneity in the distributed Cloud environment and enabling interoperability between Cloud actors. Besides, by combining our ontology with our proposed inference rules, we contribute to detect violations in the SLA contract assuring thereby the sustainability of the user satisfaction. Based on the requirements specified in the SLA contract, our Cloud Broker assists users in selecting the right provider using a multi attribute utility theory method. This method is based on utility functions representing the user satisfaction degree. To obtain accurate results, we have modelled both functional and non functional attributes utilities. We have used personalized utilities for each criterion under negotiation so that our cloud broker satisfies the best consumer requirements from functional and non functional point of view
Karlsson, Therése, and Hanna Lawrence. "English as a Second Language for Kenyan Children in Primary School : A Trial of the Spoken Language Assessment Profile – Revised Edition." Thesis, Linköpings universitet, Institutionen för klinisk och experimentell medicin, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119193.
Full textSub-Sahara Afrika är en flerspråkig miljö och det finns en brist på material för tal- och språkbedömningar inom detta område (Hartley & Krämer, 2013). Normerna för ett bedömningsinstrument kan inte användas för både enspråkiga och tvåspråkiga barn, eftersom tvåspråkiga barn kan ha varierande kunskapsnivåer inom språken (Kohnert, 2010). Spoken Language Assessment Profile – Revised edition (SLAP-R) är ett bedömningsmaterial som är avsett att utvärdera engelska som andraspråk i Sub-Sahara Afrika. Syftet med detta instrument är att försöka fylla tomrummet av lämpliga tal- och språkbedömningsmaterial som kan användas av samtliga inblandade vid att starta upp kliniker, skolor eller logopedmottagningar (Hartley & Krämer, 2013). Syftet med föreliggande studie var att undersöka engelska som andraspråk för Kenyanska barn i grundskolan baserat på deras resultat i SLAP-R. Föreliggande studie bestod av 68 deltagare med rapporterad typisk hörsel och språkutveckling som gick i klass ett eller två i en kommunal skola i västra Kenya. Alla deltagarna var mellan sex och nio år, hade ett bantuspråk som förstaspråk och hade exponerats till engelska i mindre än ett år upp till åtta år. De hade även gått i den förskolan som tillhörde deras nuvarande skola. De oberoende variablerna i föreliggande studie var klass, ålder och exponeringstid till engelska. SLAP-R består av sex deltest som testar expressiv och receptiv fonologi, semantik och grammatik. De här delarna utgör de beroende variablerna. Det finns ytterligare en del som kallas för ultimate expressive language skill (UELS) som består av sekvensbilder där deltagaren ska berätta en historia om vad som händer på bilderna. Resultatet indikerade att klass var variabeln som hade störst inverkan på deltagarnas prestationer i engelska som andraspråk. Klass två hade signifikant högre resultat gällande receptiv fonologi, såväl som expressiv och receptiv semantik och grammatik än deltagarna i klass ett. De flesta felsvaren gjordes i deltestet expressiv grammatik och var i huvudsak på grund av andra skäl än svar på kiswahili.
Books on the topic "Semantic SLAM"
Meaning: A slim guide to semantics. Boston, MA: Oxford University Press, 2011.
Find full textCognitive Semantic Study of Biblical Hebrew: The Root <i>slm</i>for Completeness-Balance. BRILL, 2021.
Find full textBook chapters on the topic "Semantic SLAM"
Qu, Xichao, and Weiqing Li. "LLN-SLAM: A Lightweight Learning Network Semantic SLAM." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 253–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36204-1_21.
Full textWebb, Andrew M., Gavin Brown, and Mikel Luján. "ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM." In Towards Autonomous Robotic Systems, 221–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23807-0_19.
Full textBai, Nanyang, Tianji Ma, Wentao Shi, and Lutao Wang. "Research on Semantic Vision SLAM Towards Dynamic Environment." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 91–102. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77569-8_7.
Full textGhorpade, Vijaya K., Dorit Borrmann, Paul Checchin, Laurent Malaterre, and Laurent Trassoudaine. "Time-of-Flight Depth Datasets for Indoor Semantic SLAM." In Springer Proceedings in Advanced Robotics, 679–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28619-4_48.
Full textZheng, Longyu, and Wenbing Tao. "Semantic Object and Plane SLAM for RGB-D Cameras." In Pattern Recognition and Computer Vision, 137–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_12.
Full textTang, Yuliang, Yingchun Fan, Shaofeng Liu, Xin Jing, Jintao Yao, and Hong Han. "Semantic SLAM Based on Joint Constraint in Dynamic Environment." In Lecture Notes in Computer Science, 27–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34110-7_3.
Full textWang, Rong, Wenzhong Zha, Xiangrui Meng, Fanle Meng, Yuhang Wu, Jianjun Ge, and Dongbing Gu. "Semantic Ground Plane Constraint in Visual SLAM for Indoor Scenes." In Pattern Recognition and Computer Vision, 268–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_22.
Full textChen, Xudong, Yu Zhu, Bingbing Zheng, and Junjian Huang. "Real-Time Semantic Mapping of Visual SLAM Based on DCNN." In Digital TV and Multimedia Communication, 194–204. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8138-6_16.
Full textJian, Rui, Weihua Su, Ruihao Li, Shiyue Zhang, Jiacheng Wei, Boyang Li, and Ruqiang Huang. "A Semantic Segmentation Based Lidar SLAM System Towards Dynamic Environments." In Intelligent Robotics and Applications, 582–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27535-8_52.
Full textSun, Ting, Dezhen Song, Dit-Yan Yeung, and Ming Liu. "Semi-semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments." In Lecture Notes in Computer Science, 63–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34995-0_6.
Full textConference papers on the topic "Semantic SLAM"
Yuan, Jiacheng, Jungseok Hong, Junaed Sattar, and Volkan Isler. "ROW-SLAM: Under-Canopy Cornfield Semantic SLAM." In 2022 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2022. http://dx.doi.org/10.1109/icra46639.2022.9811745.
Full textReid, Ian. "Towards semantic visual SLAM." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064267.
Full textYu, Chao, Zuxin Liu, Xin-Jun Liu, Fugui Xie, Yi Yang, Qi Wei, and Qiao Fei. "DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593691.
Full text"TOWARDS HUMAN INSPIRED SEMANTIC SLAM." In 7th International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002912303600363.
Full textZhang, Zheng, Decai Li, and Yuqing He. "Improved noise-adapted semantic SLAM." In 2021 3rd International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2021. http://dx.doi.org/10.1109/iai53119.2021.9619351.
Full textWang, Chongjiu, Yanduo Zhang, and Xun Li. "PMDS-SLAM: Probability Mesh Enhanced Semantic SLAM in Dynamic Environments." In 2020 5th International Conference on Control, Robotics and Cybernetics (CRC). IEEE, 2020. http://dx.doi.org/10.1109/crc51253.2020.9253465.
Full textRiazuelo, L., L. Montano, and J. M. M. Montiel. "Semantic visual SLAM in populated environments." In 2017 European Conference on Mobile Robots (ECMR). IEEE, 2017. http://dx.doi.org/10.1109/ecmr.2017.8098697.
Full textChen, Xieyuanli, Andres Milioto, Emanuele Palazzolo, Philippe Giguere, Jens Behley, and Cyrill Stachniss. "SuMa++: Efficient LiDAR-based Semantic SLAM." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967704.
Full textBowman, Sean L., Nikolay Atanasov, Kostas Daniilidis, and George J. Pappas. "Probabilistic data association for semantic SLAM." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989203.
Full textXu, Jingao, Hao Cao, Danyang Li, Kehong Huang, Chen Qian, Longfei Shangguan, and Zheng Yang. "Edge Assisted Mobile Semantic Visual SLAM." In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE, 2020. http://dx.doi.org/10.1109/infocom41043.2020.9155438.
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