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Статті в журналах з теми "Semantic SLAM"
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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.
Повний текст джерелаАнотація:
Simultaneous localization and mapping (SLAM) is a core technology for mobile robots working in unknown environments. Most existing SLAM techniques can achieve good localization accuracy in static scenes, as they are designed based on the assumption that unknown scenes are rigid. However, real-world environments are dynamic, resulting in poor performance of SLAM algorithms. Thus, to optimize the performance of SLAM techniques, we propose a new parallel processing system, named SOLO-SLAM, based on the existing ORB-SLAM3 algorithm. By improving the semantic threads and designing a new dynamic point filtering strategy, SOLO-SLAM completes the tasks of semantic and SLAM threads in parallel, thereby effectively improving the real-time performance of SLAM systems. Additionally, we further enhance the filtering effect for dynamic points using a combination of regional dynamic degree and geometric constraints. The designed system adds a new semantic constraint based on semantic attributes of map points, which solves, to some extent, the problem of fewer optimization constraints caused by dynamic information filtering. Using the publicly available TUM dataset, SOLO-SLAM is compared with other state-of-the-art schemes. Our algorithm outperforms ORB-SLAM3 in accuracy (maximum improvement is 97.16%) and achieves better results than Dyna-SLAM with respect to time efficiency (maximum improvement is 90.07%).
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You, 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.
Повний текст джерелаАнотація:
Simultaneous localization and mapping (SLAM) is one of the most essential technologies for mobile robots. Although great progress has been made in the field of SLAM in recent years, there are a number of challenges for SLAM in dynamic environments and high-level semantic scenes. In this paper, we propose a novel multimodal semantic SLAM system (MISD-SLAM), which removes the dynamic objects in the environments and reconstructs the static background with semantic information. MISD-SLAM builds three main processes: instance segmentation, dynamic pixels removal, and semantic 3D map construction. An instance segmentation network is used to provide semantic knowledge of surrounding environments in instance level. The ORB features located on the predefined dynamic objects are removed directly. In this way, MISD-SLAM effectively reduces the impact of dynamic objects to provide precise pose estimation. Then, combining multiview geometry constraint with K -means clustering algorithm, our system removes the undefined but moving pixels. Meanwhile, a 3D dense point cloud map with semantic information is reconstructed, which recovers the static background without the corruptions of dynamic objects. Finally, we evaluate MISD-SLAM by comparing to ORB-SLAM3 and the state-of-the-art dynamic SLAM systems in TUM RGB-D datasets and real-world dynamic indoor environments. The results indicate that our method significantly improves the localization accuracy and system robustness, especially in high-dynamic environments.
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Bowman, 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.
Повний текст джерелаАнотація:
Simultaneous Localization and Mapping (SLAM) has traditionally relied on representing the environment as low-level, geometric features, such as points, lines, and planes. Recent advances in object recognition capabilities, however, as well as demand for environment representations that facilitate higher-level autonomy, have motivated an object-based Semantic SLAM. We present a Semantic SLAM algorithm that directly incorporates a sparse representation of objects into a factor-graph SLAM optimization, resulting in a system that is efficient, robust to varying object shapes and environments, and easy to incorporate into an existing SLAM pipeline. Our keypoint-based representation facilitates robust detection in varying conditions and intraclass shape variation, as well as computational efficiency. We demonstrate the performance of our algorithm in two different SLAM systems and in varying environments.
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Guan, 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.
Повний текст джерелаАнотація:
Visual simultaneously localization and mapping (SLAM) is important for self-localization and environment perception of service robots, where semantic SLAM can provide a more accurate localization result and a map with abundant semantic information. In this article, we propose a real-time PO-SLAM approach with the combination of both point and object measurements. With point–point association in ORB-SLAM2, we also consider point–object association based on object segmentation and object–object association, where the object segmentation is employed by combining object detection with depth histogram. Also, besides the constraint of feature points belonging to an object, a semantic constraint of relative position invariance among objects is introduced. Accordingly, two semantic loss functions with point and object information are designed and added to the bundle adjustment optimization. The effectiveness of the proposed approach is verified by experiments.
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Han, 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.
Повний текст джерела
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Long, 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.
Повний текст джерелаАнотація:
Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional to the recognition accuracy. To simultaneously enhance the speed and accuracy for recognizing dynamic objects in different environments, a novel SLAM system without prior semantics called DGFlow-SLAM is proposed in this paper. A novel grid segmentation method is used in the system to segment the scene flow, and then an adaptive threshold method is used to roughly detect the dynamic objects. Based on this, a deep mean clustering segmentation method is applied to find potential dynamic targets. Finally, the results of grid segmentation and depth mean clustering segmentation are jointly used to find moving objects accurately, and all the feature points of the moving objects are removed on the premise of retaining the static part of the moving object. The experimental results show that on the dynamic sequence dataset of TUM RGB-D, compared with the DynaSLAM system with the highest accuracy for detecting moderate and violent motion and the DS-SLAM with the highest accuracy for detecting slight motion, DGflow-SLAM obtains similar accuracy results and improves the accuracy by 7.5%. In addition, DGflow-SLAM is 10 times and 1.27 times faster than DynaSLAM and DS-SLAM, respectively.
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Jia, 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.
Повний текст джерелаАнотація:
With the development of intelligent concepts in various fields, research on driverless and intelligent industrial robots has increased. Vision-based simultaneous localization and mapping (SLAM) is a widely used technique. Most conventional visual SLAM algorithms are assumed to work in ideal static environments; however, such environments rarely exist in real life. Thus, it is important to develop visual SLAM algorithms that can determine their own positions and perceive the environment in real dynamic environments. This paper proposes a lightweight robust dynamic SLAM system based on a novel semantic segmentation network (LRD-SLAM). In the proposed system, a fast deep convolutional neural network (FNet) is implemented into ORB-SLAM2 as a semantic segmentation thread. In addition, a multiview geometry method is introduced, in which the accuracy of detecting dynamic points is further improved through the difference in parallax angle and depth, and the information of the keyframes is used to repair the static background information absent from the removal of dynamic objects, to facilitate the subsequent reconstruction of the point cloud map. Experimental results obtained using the TUM RGB-D dataset demonstrate that the proposed system improves the positioning accuracy and robustness of visual SLAM in indoor pedestrian dynamic environments.
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Fan, 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.
Повний текст джерела
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Miao, 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.
Повний текст джерелаАнотація:
A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.
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Wu, 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.
Повний текст джерелаАнотація:
The Simultaneous Localization and Mapping (SLAM) algorithm is a hotspot in robot application research with the ability to help mobile robots solve the most fundamental problems of “localization” and “mapping”. The visual semantic SLAM algorithm fused with semantic information enables robots to understand the surrounding environment better, thus dealing with complexity and variability of real application scenarios. DS-SLAM (Semantic SLAM towards Dynamic Environment), one of the representative works in visual semantic SLAM, enhances the robustness in the dynamic scene through semantic information. However, the introduction of deep learning increases the complexity of the system, which makes it a considerable challenge to achieve the real-time semantic SLAM system on the low-power embedded platform. In this paper, we realized the high energy-efficiency DS-SLAM algorithm on the Field Programmable Gate Array (FPGA) based heterogeneous platform through the optimization co-design of software and hardware with the help of OpenCL (Open Computing Language) development flow. Compared with Intel i7 CPU on the TUM dataset, our accelerator achieves up to 13× frame rate improvement, and up to 18× energy efficiency improvement, without significant loss in accuracy.
Дисертації з теми "Semantic SLAM"
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Salas-Moreno, Renato F. "Dense semantic SLAM." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24524.
Повний текст джерелаАнотація:
Simultaneous Localisation and Mapping (SLAM) began as a technique to enable real-time robotic navigation on previously unexplored environments. The created maps however were designed for the sole purpose of localising the robot (i.e. what is the position and orientation of the robot in relation to the map) and several systems demonstrated the increasing descriptive power of map representations, which on vision-only SLAM solutions consisted of simple sparse corner-like features as well as edges, planes and most recently fully dense surfaces that abandon the notion of sparse structures altogether. Early sparse representations enjoyed the benefit of being simple to maintain as features could be added, optimised and removed independently while being memory and compute efficient, making them suitable for robust real-time camera tracking that relies on a consistent map. However, sparse representations are limiting when it comes to interaction, as for example, a robot aiming to safely navigate in an environment would need to sense complete surfaces in addition to empty space. Furthermore, sparse features can only be detected on highly-textured areas and during slow motion. Recent dense methods overcome the limitations of sparse methods as they can work in situations where corner features would fail to be detected due to blurry images created during rapid camera motion and also enable to correctly reason about occlusions and complete 3D surfaces, thus raising the interaction capabilities to new levels. This is only possible thanks to the advent of commodity parallel processing power and large amount of memory on Graphic Processing Units (GPUs) that needs careful consideration during algorithm design. However, increasing the map density makes creating consistent structures more challenging due to the vast amount of parameters to optimise and the interdependencies amongst them. More importantly, our interest is in making interaction even more sophisticated by abandoning the idea that an environment is a dense monolithic structure in favour of one composed of discrete detachable objects and bounded regions having physical properties and metadata. This work explores the development of a new type of visual SLAM system representing the map with semantically meaningful objects and planar regions which we call Dense Semantic SLAM, enabling new types of interaction where applications that can go beyond asking the question of "where am I" towards "what is around me and what can I do with it". In a way it can be seen as a return to lightweight sparse-based representations while keeping the predictive power of dense methods with added scene understanding at the object and region levels.
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Baxter, 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.
Повний текст джерелаАнотація:
Thesis: Nav. E., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020Thesis: 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
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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.
Повний текст джерелаАнотація:
Pour agir de manière autonome et intelligente dans un environnement, un robot mobile doit disposer de cartes. Une carte contient les informations spatiales sur l’environnement. La géométrie 3D ainsi connue par le robot est utilisée non seulement pour éviter la collision avec des obstacles, mais aussi pour se localiser et pour planifier des déplacements. Les robots de prochaine génération ont besoin de davantage de capacités que de simples cartographies et d’une localisation pour coexister avec nous. La quintessence du robot humanoïde de service devra disposer de la capacité de voir comme les humains, de reconnaître, classer, interpréter la scène et exécuter les tâches de manière quasi-anthropomorphique. Par conséquent, augmenter les caractéristiques des cartes du robot à l’aide d’attributs sémiologiques à la façon des humains, afin de préciser les types de pièces, d’objets et leur aménagement spatial, est considéré comme un plus pour la robotique d’industrie et de services à venir. Une carte sémantique enrichit une carte générale avec les informations sur les entités, les fonctionnalités ou les événements qui sont situés dans l’espace. Quelques approches ont été proposées pour résoudre le problème de la cartographie sémantique en exploitant des scanners lasers ou des capteurs de temps de vol RGB-D, mais ce sujet est encore dans sa phase naissante. Dans cette thèse, une tentative de reconstruction sémantisée d’environnement d’intérieur en utilisant une caméra temps de vol qui ne délivre que des informations de profondeur est proposée. Les caméras temps de vol ont modifié le domaine de l’imagerie tridimensionnelle discrète. Elles ont dépassé les scanners traditionnels en termes de rapidité d’acquisition des données, de simplicité fonctionnement et de prix. Ces capteurs de profondeur sont destinés à occuper plus d’importance dans les futures applications robotiques. Après un bref aperçu des approches les plus récentes pour résoudre le sujet de la cartographie sémantique, en particulier en environnement intérieur. Ensuite, la calibration de la caméra a été étudiée ainsi que la nature de ses bruits. La suppression du bruit dans les données issues du capteur est menée. L’acquisition d’une collection d’images de points 3D en environnement intérieur a été réalisée. La séquence d’images ainsi acquise a alimenté un algorithme de SLAM pour reconstruire l’environnement visité. La performance du système SLAM est évaluée à partir des poses estimées en utilisant une nouvelle métrique qui est basée sur la prise en compte du contexte. L’extraction des surfaces planes est réalisée sur la carte reconstruite à partir des nuages de points en utilisant la transformation de Hough. Une interprétation sémantique de l’environnement reconstruit est réalisée. L’annotation de la scène avec informations sémantiques se déroule sur deux niveaux : l’un effectue la détection de grandes surfaces planes et procède ensuite en les classant en tant que porte, mur ou plafond; l’autre niveau de sémantisation opère au niveau des objets et traite de la reconnaissance des objets dans une scène donnée. A partir de l’élaboration d’une signature de forme invariante à la pose et en passant par une phase d’apprentissage exploitant cette signature, une interprétation de la scène contenant des objets connus et inconnus, en présence ou non d’occultations, est obtenue. Les jeux de données ont été mis à la disposition du public de la recherche universitaireIntelligent 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
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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/.
Повний текст джерелаАнотація:
Con questa dissertazione di tesi miro ad illustrare i risultati della mia ricerca nel campo del Semantic Publishing, consistenti nello sviluppo di un insieme di metodologie, strumenti e prototipi, uniti allo studio di un caso d‟uso concreto, finalizzati all‟applicazione ed alla focalizzazione di Lenti Semantiche (Semantic Lenses).
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Rogers, John Gilbert. "Life-long mapping of objects and places in domestic environments." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47736.
Повний текст джерелаАнотація:
In the future, robots will expand from industrial and research applications to the home. Domestic service robots will work in the home to perform useful tasks such as object retrieval, cleaning, organization, and security. The tireless support of these systems will not only enable able bodied people to avoid mundane chores; they will also enable the elderly to remain independent from institutional care by providing service, safety, and companionship. Robots will need to understand the relationship between objects and their environments to perform some of these tasks. Structured indoor environments are organized according to architectural guidelines and convenience for their residents. Utilizing this information makes it possible to predict the location of objects. Conversely, one can also predict the function of a room from the detection of a few objects within a given space.
This thesis introduces a framework for combining object permanence and context called the probabilistic cognitive model. This framework combines reasoning about spatial extent of places and the identity of objects and their relationships to one another and to the locations where they appear. This type of reasoning takes into account the context in which objects appear to determine their identity and purpose. The probabilistic cognitive model combines a mapping system called OmniMapper with a conditional random field probabilistic model for context representation. The conditional random field models the dependencies between location and identity in a real-world domestic environment. This model is used by mobile robot systems to predict the effects of their actions during autonomous object search tasks in unknown environments.
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Trevor, 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.
Повний текст джерелаАнотація:
Although much progress has been made in the field of robotic mapping, many challenges remain including: efficient semantic segmentation using RGB-D sensors, map representations that include complex features (structures and objects), and interfaces for interactive annotation of maps. This thesis addresses how prior knowledge of semi-structured human environments can be leveraged to improve segmentation, mapping, and semantic annotation of maps. We present an organized connected component approach for segmenting RGB-D data into planes and clusters. These segments serve as input to our mapping approach that utilizes them as planar landmarks and object landmarks for Simultaneous Localization and Mapping (SLAM), providing necessary information for service robot tasks and improving data association and loop closure. These features are meaningful to humans, enabling annotation of mapped features to establish common ground and simplifying tasking. A modular, open-source software framework, the OmniMapper, is also presented that allows a number of different sensors and features to be combined to generate a combined map representation, and enabling easy addition of new feature types.
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Salehi, 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.
Повний текст джерелаАнотація:
La fusion entre un ensemble de capteurs et de bases de données dont les erreurs sont indépendantes est aujourd’hui la solution la plus fiable et donc la plus répandue de l’état de l’art au problème de la localisation. Les véhicules semi-autonomes et autonomes actuels, ainsi que les applications de réalité augmentée visant les contextes industriels exploitent des graphes de capteurs et de bases de données de tailles considérables, dont la conception, la calibration et la synchronisation n’est, en plus d’être onéreuse, pas triviale. Il est donc important afin de pouvoir démocratiser ces technologies, d’explorer la possibilité de l’exploitation de capteurs et bases de données bas-coûts et aisément accessibles. Cependant, ces sources d’information sont naturellement plus incertaines, et plusieurs obstacles subsistent à leur utilisation efficace en pratique. De plus, les succès récents mais fulgurants des réseaux profonds dans des tâches variées laissent penser que ces méthodes peuvent représenter une alternative peu coûteuse et efficace à certains modules des systèmes de SLAM actuels. Dans cette thèse, nous nous penchons sur la localisation à grande échelle d’un véhicule dans un repère géoréférencé à partir d’un système bas-coût. Celui-ci repose sur la fusion entre le flux vidéo d’une caméra monoculaire, des modèles 3d non-texturés mais géoréférencés de bâtiments,des modèles d’élévation de terrain et des données en provenance soit d’un GPS bas-coût soit de l’odométrie du véhicule. Nos travaux sont consacrés à la résolution de deux problèmes. Le premier survient lors de la fusion par terme barrière entre le VSLAM et l’information de positionnement fournie par un GPS bas-coût. Cette méthode de fusion est à notre connaissance la plus robuste face aux incertitudes du GPS, mais est plus exigeante en matière de ressources que la fusion via des fonctions de coût linéaires. Nous proposons une optimisation algorithmique de cette méthode reposant sur la définition d’un terme barrière particulier. Le deuxième problème est le problème d’associations entre les primitives représentant la géométrie de la scène(e.g. points 3d) et les modèles 3d des bâtiments. Les travaux précédents se basent sur des critères géométriques simples et sont donc très sensibles aux occultations en milieu urbain. Nous exploitons des réseaux convolutionnels profonds afin d’identifier et d’associer les éléments de la carte correspondants aux façades des bâtiments aux modèles 3d. Bien que nos contributions soient en grande partie indépendantes du système de SLAM sous-jacent, nos expériences sont basées sur l’ajustement de faisceaux contraint basé images-clefs. Les solutions que nous proposons sont évaluées sur des séquences de synthèse ainsi que sur des séquence urbaines réelles sur des distances de plusieurs kilomètres. Ces expériences démontrent des gains importants en performance pour la fusion VSLAM/GPS, et une amélioration considérable de la robustesse aux occultations dans la définition des contraintesThe 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
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Drouilly, Romain. "Cartographie hybride métrique topologique et sémantique pour la navigation dans de grands environnements." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4037/document.
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La navigation autonome est l'un des plus grands challenges pour un robot autonome. Elle nécessite la capacité à localiser sa position ou celle de l'objectif et à trouver le meilleur chemin connectant les deux en évitant les obstacles. Pour cela, les robots utilisent une carte de l'environnement modélisant sa géométrie ou sa topologie. Cependant la construction d'une telle carte dans des environnements de grande dimension est ardue du fait de la quantité de données à traiter et le problème de la localisation peut devenir insoluble. De plus, un environnement changeant peut conduire à l'obsolescence rapide du modèle. Comme démontré dans cette thèse, l'ajout d'information de nature sémantique dans ces cartes améliore significativement les performances de navigation des robots dans des environnements réels. La labélisation d'image permet de construire des modèles extrêmement compacts qui sont utilisés pour la localisation rapide en utilisant une approche basée comparaison de graphes. Ils sont des outils puissants pour comprendre l'environnement et permettent d'étendre la carte au-delà des limites perceptuelles du robot. L'analyse statistique de ces modèles est utilisée pour construire un embryon de sens commun qui est ensuite utilisé pour détecter des erreurs de labélisation et pour mettre à jour la carte en utilisant des algorithmes conçus pour maintenir une représentation stable en dépits des occlusions créées par les objets dynamiques. Finalement, la sémantique est utilisées pour sélectionner le meilleur chemin vers une position cible en fonction de critères de haut niveau plutôt que métriques, autorisant une navigation intelligenteUtonomous 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
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Fakhfakh, Inès. "Semantic based cloud broker architecture optimizing users satisfaction." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0008/document.
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Le Cloud Computing est un nouveau modèle économique hébergeant les applications de la technologie de l’information. Le passage au Cloud devient un enjeu important des entreprises pour des raisons économiques. La nature dynamique et la complexité croissante des architectures de Cloud impliquent plusieurs défis de gestion. Dans ce travail, nous nous intéressons à la gestion des contrats SLA. Vu le manque de standardisation, chaque fournisseur de service décrit les contrats SLA avec son propre langage, ce qui laisse l'utilisateur perplexe concernant le choix de son fournisseur de services. Dans ce travail, nous proposons une architecture de Cloud Broker permettant d’établir et de négocier les contrats SLA entre les fournisseurs et les consommateurs du Cloud. L’objectif de cette architecture est d’aider l’utilisateur à trouver le meilleur fournisseur en utilisant une méthode multi-critère. Cette méthode considère chaque critère comme une fonction d’utilité à intégrer dans une super-fonction d’utilité. Nous proposons d’illustrer chaque fonction d’utilité par une courbe spécifique à lui représentant bien le critère de choix. Nous essayons de cerner la plupart des critères qui contribuent dans le choix du meilleurs service et de les classer en critères fonctionnels et critères non fonctionnels. Les contrats SLA établit par notre broker sont formalisés sous forme d’ontologies qui permettent de masquer l'hétérogénéité et d’assurer l'interopérabilité entre les acteurs du Cloud. En outre, l’utilisation des règles d'inférence nous a permis de détecter les violations dans le contrat SLA établit et de garantir ainsi le respect de la satisfaction client dans le tempsCloud 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
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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.
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Sub-Saharan Africa is a multilingual environment and there is a lack of materials available for speech and language assessment in this area (Hartley & Krämer, 2013). The norms for assessment material cannot be used for both monolinguals and bilinguals, since bilinguals may have different levels of knowledge in their languages (Kohnert, 2010). The Spoken Language Assessment Profile – Revised edition (SLAP-R) is an assessment that can be used to evaluate English as a second language (ESL) in Sub-Saharan Africa. The purpose of this instrument is an attempt to fill the gap of suitable speech and language assessment tools that can be used for all those involved in setting up clinics, schools or speech and language assessment tools (Hartley & Krämer, 2013). The aim of the present study was to assess English as a second language for Kenyan children in primary school based on their result on the SLAP-R. The present study consisted of 68 participants with reported typically developed language and hearing that attended first or second grade in a public school in western Kenya. All participants were between six and nine years old, had a Bantu language as their first language and had been exposed to English for less than one year up to eight years. They had also attended preschool at their current school. The independent variables in the present study were grade, age and exposure to English. SLAP-R consists of six subtests that test expressive and receptive phonology, semantics and grammar. These parts constituted the dependent variables. In addition there is a part called ultimate expressive language skill (UELS) that consists of picture sequences where the participant should tell a story of what is happening in the pictures. The result indicated that grade had the largest effect on the participant’s performance in English as a second language. Grade two had significantly higher results regarding receptive phonology as well as expressive and receptive semantics and grammar than the participants in grade one. Most of the incorrect answers were made in the subtest expressive grammar. These answers were mainly incorrect due to other reasons than an answer in Kiswahili.Sub-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.
Книги з теми "Semantic SLAM"
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Meaning: A slim guide to semantics. Boston, MA: Oxford University Press, 2011.
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Cognitive Semantic Study of Biblical Hebrew: The Root <i>slm</i>for Completeness-Balance. BRILL, 2021.
Знайти повний текст джерелаЧастини книг з теми "Semantic SLAM"
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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.
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Webb, 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.
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Bai, 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.
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Ghorpade, 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.
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Zheng, 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.
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Tang, 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.
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Wang, 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.
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Chen, 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.
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Jian, 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.
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Sun, 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.
Повний текст джерелаТези доповідей конференцій з теми "Semantic SLAM"
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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.
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Reid, 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.
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Yu, 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.
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"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.
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Zhang, 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.
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Wang, 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.
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Riazuelo, 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.
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Chen, 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.
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Bowman, 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.
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Xu, 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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