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Добірка наукової літератури з теми "Indoor robotics"

Автор: Grafiati
Опубліковано: 22 березня 2025

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Статті в журналах з теми "Indoor robotics"

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Cooper, Martin. "Paw-Sitive Reception for Robot Guide Dog." ITNOW 66, no. 2 (May 20, 2024): 30–31. http://dx.doi.org/10.1093/itnow/bwae048a.

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Abstract Working in partnership with charities, AI and robotics experts from the University of Glasgow have demonstrated a robotic guide dog designed to help partially sighted people navigate indoor spaces.
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Cooper, Martin. "Paw-Sitive Reception for Robot Guide Dog." ITNOW 66, no. 2 (May 1, 2024): 30–31. http://dx.doi.org/10.1093/itnow/bwae048.

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Abstract Working in partnership with charities, AI and robotics experts from the University of Glasgow have demonstrated a robotic guide dog designed to help partially sighted people navigate indoor spaces.
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3

Wang, Jianguo, Shiwei Lin, and Ang Liu. "Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review." Biomimetics 8, no. 4 (August 7, 2023): 350. http://dx.doi.org/10.3390/biomimetics8040350.

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Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.
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Dahri, Fida Hussain, Ghulam E. Mustafa Abro, Nisar Ahmed Dahri, Asif Ali Laghari, and Zain Anwar Ali. "Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition." IECE Transactions on Intelligent Systematics 2, no. 1 (December 27, 2024): 14–26. https://doi.org/10.62762/tis.2025.613103.

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Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor picture comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a training accuracy of 99%, a testing accuracy of 89.73%, a precision of 90.11%, a recall of 89.73%, and an F1-score of 89.79%. These findings underscore the efficacy of our methodology in tackling the intricacies of indoor scene recognition. This research substantially advances the domain of robotics and automation by establishing a more resilient and dependable framework for autonomous navigation and scene comprehension in GPS-denied settings, facilitating the development of more efficient and intelligent robotic systems.
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5

Caro, Luis, Javier Correa, Pablo Espinace, Daniel Langdon, Daniel Maturana, Ruben Mitnik, Sebastian Montabone, et al. "Indoor Mobile Robotics at Grima, PUC." Journal of Intelligent & Robotic Systems 66, no. 1-2 (July 20, 2011): 151–65. http://dx.doi.org/10.1007/s10846-011-9604-2.

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6

Frías, E., J. Balado, L. Díaz-Vilariño, and H. Lorenzo. "POINT CLOUD ROOM SEGMENTATION BASED ON INDOOR SPACES AND 3D MATHEMATICAL MORPHOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W1-2020 (September 3, 2020): 49–55. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w1-2020-49-2020.

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Abstract. Room segmentation is a matter of ongoing interesting for indoor navigation and reconstruction in robotics and AEC. While in robotics field, the problem room segmentation has been typically addressed on 2D floorplan, interest in enrichment 3D models providing more detailed representation of indoors has been growing in the AEC. Point clouds make available more realistic and update but room segmentation from point clouds is still a challenging topic. This work presents a method to carried out point cloud segmentation into rooms based on 3D mathematical morphological operations. First, the input point cloud is voxelized and indoor empty voxels are extracted by CropHull algorithm. Then, a morphological erosion is performed on the 3D image of indoor empty voxels in order to break connectivity between voxels belonging to adjacent rooms. Remaining voxels after erosion are clustered by a 3D connected components algorithm so that each room is individualized. Room morphology is retrieved by individual 3D morphological dilation on clustered voxels. Finally, unlabelled occupied voxels are classified according proximity to labelled empty voxels after dilation operation. The method was tested in two real cases and segmentation performance was evaluated with encouraging results.
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Jimenez Builes, Jovani Alberto, Gustavo Acosta Amaya, and Julián López Velásquez. "Autonomous navigation and indoor mapping for a service robot." Investigación e Innovación en Ingenierías 11, no. 2 (September 22, 2023): 28–38. http://dx.doi.org/10.17081/invinno.11.2.6459.

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Abstract Objective: Simultaneous Localization and Mapping (SLAM) is a quite common and interesting problem in mobile robotics. It is the basis of safe autonomous navigation of mobile robots and the entrance to new combined applications with a manipulator for instance. Method: In order to find a solution to the SLAM problem, the ROS middleware and the MRPT were selected. Autonomous navigation was tested using two methods, the MRPT navigation ROS package, which is a reactive navigation method based on Trajectory Parameter Space (TP-Space) transformations, and the ROS navigation stack, a standard for differential drive and holonomic wheeled robots. Results: To validate the advantages and disadvantages of both approaches, a mobile robot with strong kinematic constraints (Ackermann-steering-type) known as Summit was used. As an additional work, an application using the mobile robot Summit and a robotic manipulator (Powerball) was carried out, with the intention of picking and placing objects of the mobile robot, a widely spread application among service robotics, especially, in the area of industrial logistics. Conclusions: Finally, it is concluded that with the tests carried out with the robot, it was possible to demonstrate autonomous navigation, using the two mentioned methods.
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Tajti, Ferenc, Géza Szayer, Bence Kovács, and Mauricio A. P. Burdelis. "Mobile Robot Performance Analysis for Indoor Robotics." Periodica Polytechnica Civil Engineering 59, no. 2 (2015): 123–31. http://dx.doi.org/10.3311/ppci.7759.

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Vekhter, Joshua, and Joydeep Biswas. "Responsible Robotics: A Socio-Ethical Addition to Robotics Courses." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15877–85. http://dx.doi.org/10.1609/aaai.v37i13.26885.

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Анотація:
We are witnessing a rapid increase in real-world autonomous robotic deployments in environments ranging from indoor homes and commercial establishments to large-scale urban areas, with applications ranging from domestic assistance to urban last-mile delivery. The developers of these robots inevitably have to make impactful design decisions to ensure commercially viability, but such decisions have serious real-world consequences. Unfortunately it is not uncommon for such projects to face intense bouts of social backlash, which can be attributed to a wide variety of causes, ranging from inappropriate technical design choices to transgressions of social norms and lack of community engagement. To better prepare students for the rigors of developing and deploying real-world robotics systems, we developed a Responsible Robotics teaching module, intended to be included in upper-division and graduate level robotics courses. Our module is structured as a role playing exercise which aims to equip students with a framework for navigating the conflicting goals of human actors which govern robots in the field. We report on instructor reflections and anonymous survey responses from offering our responsible robotics module in both a graduate-level, and an upper-division undergraduate robotics course at UT Austin. The responses indicate that students gained a deeper understanding of the socio-technical factors of real-world robotics deployments than they might have using self-study methods, and the students proactively suggested that such modules should be more broadly included in CS courses.
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MANO, Marsel, and Genci CAPI. "1A2-D03 Adaptive navigation of a brain controlled robotic wheelchair in an indoor environment(Rehabilitation Robotics and Mechatronics (2))." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _1A2—D03_1—_1A2—D03_4. http://dx.doi.org/10.1299/jsmermd.2013._1a2-d03_1.

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Дисертації з теми "Indoor robotics"

1

Vojta, Jakub. "Bezpečnost provozu mobilních robotů v indoor prostředí." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-232641.

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During cooperation with the Bender Robotics company a need for operational safety assessment of an autonomous mobile robot (AMR) emerged. Operational safety evaluation is a step towards mass production of the studied robot. Market entry of a product requires a string of various actions and safety assessment is one of them. For risk identification and severity rating were used legal requirements, best practice given by standards, FMEA method, experiment and RIPRAN method. Threats, possible scenarios and risks analysis is systematically discussed through all areas of operation of the robot, from design and construction to control software. All the steps are described in logical order. Starting with information research, going on with series of analysis and ending with suggestions for increased operational safety of autonomous mobile robots.
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Pettersson, Rasmus. "Continuous localization in indoor shifting environment." Thesis, Uppsala universitet, Fasta tillståndets elektronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326270.

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In this Master Thesis different approaches to mobile localization within construction environments are investigated. At first an overview of different sensors commonly used within localization is presented together with different map representations and a system consisting of a laser scanner and wheel encoders is chosen. The hardware is prepared for the open source ROS environment and three different algorithms for localization are tested. Two algorithms, Gmapping and HectorSLAM, used for Simultaneous Localization and Mapping, are compared. The best map is then used by a Monte Carlo localization algorithm, AMCL, for autonomous navigation. It is found that HectorSLAM produces the most accurate map, given that the grid refinement level is fine enough for the environment. It is also found that the maximum Kullback Leiber distance, used in AMCL, needs to be calibrated in order to perform a sufficient navigation.
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Perko, Eric Michael. "Precision Navigation for Indoor Mobile Robots." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1345513785.

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4

Yang, Yin. "Nonlinear control and state estimation of holonomic indoor airship." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106573.

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Three full-state optimal controllers are proposed to fulfill the requirements of flying an indoor holonomic airship in real-time, namely, hovering control, set-point control and continuous reference tracking. In the hovering control design, the airship is assumed to be a quasi stationary plant, and an infinite horizon linear quadratic regulator (LQR) operating in again scheduling manner is employed. Meanwhile, a controller based on the state-dependent Riccati equation (SDRE) and ad hoc feedforward compensation is synthesized to tackle the set-point control problem. Lastly, a continuous tracker is dedicated to rejecting alldisturbances along any given reference trajectory. With reasonable computation cost, the proposed controllers show significant advantages over the PD controller in both simulationand real flights. A state estimator designed with the unscented Kalman filter is also implemented in this work. The purpose is to track the airship state for the feedback loop and other navigation tasks by fusing information from the on-board (an inertia measurement unit and a laser range finder) and/or o-board (an infra-red based motion capture system) sensors. A loosely coupled sensor fusion scheme is employed and validated in experiments.
Trois méthodes optimales de commande à retour d'état complet sont proposées ici afin d'accomplir les exigences du vol intérieur d'un ballon dirigeable holonomique, et ceci, en temps réel. Les manoeuvres exigées incluent le maintien d'une position stationnaire, le mouvement vers un point et suivant une trajectoire continue. Pour la régularisation, un modèle quasi-stationnaire du ballon est assumé et un régulateur quadratique-linéaire (LQR) à horizon infini est utilisé dans un mode d'échelonnage des gains. De plus, les mouvements vers un point sont accomplis en se basant sur le retour d'état pour résoudre l'équation de Riccati qui en dépend et pour compenser la dynamique non-linéaire. Finalement, les perturbations autour d'une trajectoire continue sont rejetées par une méthode dédiée afin de suivre cette trajectoire. Preuves expérimentales et simulées à l'appui, ces méthodes de commande démontrent des avantages significatifs par rapport aux méthodes classiques de commande porportionelle-dérivée (PD), et ceci, avec des exigences modérées sur le système informatique. Ce travail de thèse démontre aussi l'utilisation d'un filtre de Kalman non-parfumé (UKF) pour estimer l'état du système. Cette estimation produit le retour d'ètat complet nècessaire aux mèthodes de commande et à d'autres tâches de navigation en combinant les mesures de différents systèmes enbarqués (système inertiel et télédétecteur par laser) et non-embarqués (système de capture du mouvement à l'infra-rouge). Une méthode de fusion sensorielle à séparation partielle est utilisée et validée expérimentalement.
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Gandhi, Anall Vijaykumar. "An Accuracy Improvement Method for Cricket Indoor Location System." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1369316496.

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Valdmanis, Mikelis. "Localization and navigation of a holonomic indoor airship using on-board sensors." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97204.

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Two approaches to navigation and localization of a holonomic, unmanned, indoor airship capable of 6-degree-of-freedom (DOF) motion using on-board sensors are presented. First, obstacle avoidance and primitive navigation were attempted using a light-weight video camera. Two optical flow algorithms were investigated. Optical flow estimates the motion of the environment relative to the camera by computing temporal and spatial fluctuations of image brightness. Inferences on the nature of the visible environment, such as obstacles, would then be made based on the optical flow field. Results showed that neither algorithm would be adequate for navigation of the airship.Localization of the airship in a restricted state space – three translational DOF and yaw rotation – and a known environment was achieved using an advanced Monte Carlo Localization (MCL) algorithm and a laser range scanner. MCL is a probabilistic algorithm that generates many random estimates, called particles, of potential airship states. During each operational time step each particle's location is adjusted based on airship motion estimates and particles are assigned weights by evaluating simulated sensor measurements for the particles' poses against the actual measurements. A new set of particles is drawn from the previous set with probability proportional to the weights. After several time steps the set converges to the true position of the airship. The MCL algorithm achieves global localization, position tracking, and recovery from the "kidnapped robot" problem. Results from off-line processing of airship flight data, using MCL, are presented and the possibilities for on-line implementation are discussed.
Deux approches de navigation et localisation d'un drone intérieur équipé de capteurs et capable de six degrés de liberté seront présentées. Premièrement, des vols ayant comme simple but d'éviter des obstacles et de naviguer le drone ont été exécutés à l'aide d'une caméra vidéo. Deux algorithmes de flux optique ont été étudiés. Le flux optique estime le déplacement de l'environnement relatif à la caméra en calculant les variations dans la clarté de l'image. Les traits caractéristiques de l'environnement, comme les obstacles, sont alors déterminés en se basant sur le champ de flux optique. Les résultats démontrent que ni l'un ni l'autre des algorithmes sont adéquats pour naviguer le drone.La localisation du drone dans une représentation d'état, caractérisée par trois degrés de liberté en translation et par la vitesse de lacet, ainsi que dans un environnement connu a été accomplie en utilisant l'algorithme avancé de Localisation Monte Carlo (MCL) et un télémètre laser. MCL est un algorithme probabiliste qui génère aléatoirement plusieurs estimés, nommés particules, d'états potentiels du drone. À chaque incrément de temps, la position de chaque particule est ajustée selon les déplacements estimés du drone et ces particules sont pondérées en comparant les valeurs estimées du capteur avec les valeurs actuelles. Ensuite, un nouvel ensemble de particules est créé à partir du précédent en considérant la pondération des particules. Après plusieurs incréments de temps, l'ensemble converge vers la position réelle du drone. L'algorithme MCL accompli alors une localisation globale, un suivi de position et une résolution du problème du robot « kidnappé ». L'analyse hors-ligne des résultats avec l'algorithme MCL est présentée et les possibilités d'implémenter cette méthode en ligne sont discutées.
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Szenher, Matthew D. "Visual homing in dynamic indoor environments." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3193.

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Our dissertation concerns robotic navigation in dynamic indoor environments using image-based visual homing. Image-based visual homing infers the direction to a goal location S from the navigator’s current location C using the similarity between panoramic images IS and IC captured at those locations. There are several ways to compute this similarity. One of the contributions of our dissertation is to identify a robust image similarity measure – mutual image information – to use in dynamic indoor environments. We crafted novel methods to speed the computation of mutual image information with both parallel and serial processors and demonstrated that these time-savers had little negative effect on homing success. Image-based visual homing requires a homing agent tomove so as to optimise themutual image information signal. As the mutual information signal is corrupted by sensor noise we turned to the stochastic optimisation literature for appropriate optimisation algorithms. We tested a number of these algorithms in both simulated and real dynamic laboratory environments and found that gradient descent (with gradients computed by one-sided differences) works best.
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Fernandez, labrador Clara. "Indoor Scene Understanding using Non-Conventional Cameras." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCK037.

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Les humains sont en mesure d’interpréter l’environnement qui les entourent avec peu d’effort grâce à système visuel très performant. Par analogie, un système de vision capable de recueillir les mêmes informations sur l’environnement est hautement souhaitable en robotique autonome pour effectuer des tâches complexes et ainsi interagir avec les humains.À cet égard, nous nous sommes particulièrement intéressés aux environnements intérieurs, dans lesquels les humains passent presque toute leur vie. Dans ce travail, pour faire une analyse efficace et rapide des scènes, nous avons opté pour l’utilisation de caméras non conventionnelles : l’imagerie 360° et les capteurs 3D. Ces systèmes ont la particularité d’acquérir en une seule prise de vue soit la totalité de l’environnement qui entoure le robot (caméras 360°) soit l’information 3D.C’est ainsi que cette thèse aborde les problèmes de description hiérarchique d’une scène d’intérieur avec des capteurs non conventionnels allant de l’estimation de la disposition des pièces ; de la détection et la localisation des objets à la modélisation de la forme des objets 3D.Ces différents points font l’objet de contribution dans ce travail. Dans un premier temps, nous nous sommes intéressés à l'estimation de la disposition 3D de la pièce à partir d'une seule image à 360°. Pour ce faire, nous exploitons l'hypothèse de Manhattan World et les techniques d'apprentissage profond pour proposer des modèles qui gèrent les parties occultées de la pièce sur l'image. A vu de la particularité des images considérées, nous avons développé de nouveaux filtres de convolution d’image tenant compte des fortes distorsions des images équirectangulaires.Par la suite, et dans l’objectif de permettre au robot de faire une analyse contextuelle de hauts niveaux de la scène qui l’entoure, nous nous sommes intéressés au problème de la localisation et de la segmentation des objets. C’est ainsi que nous avons une nouvelle fois exploité les images 360° en tenant compte de la disposition des objets 2D dans l’image dans le but de les décrire par leur modèle 3D en adéquation avec la disposition de la pièce préalablement estimée.La dernière contribution de ce travail tire parti des capteurs 3D pour étudier la forme des objets. Dans ce cadre, nous utilisons une modélisation explicite de la non-rigidité de objets et caractérisons leurs symétries afin de détecter, par un apprentissage profond non supervisé, ces points d’intérêt 3D.Toutes ces contributions nous ont permis de faire progresser l’état de l’art sur les problèmes posés et ont toutes fait l’objet d’évaluation sur des bases de données de référence dans notre communauté
Humans understand environments effortlessly, under a wide variety of conditions, by the virtue of visual perception. Computer vision for similar visual understanding is highly desirable, so that machines can perform complex tasks by interacting with the real world, to assist or entertain humans. In this regard, we are particularly interested in indoor environments, where humans spend nearly all their lifetime.This thesis specifically addresses the problems that arise during the quest of the hierarchical visual understanding of indoor scenes.On the side of sensing the wide 3D world, we propose to use non-conventional cameras, namely 360º imaging and 3D sensors. On the side of understanding, we aim at three key aspects: room layout estimation; object detection, localization and segmentation; and object category shape modeling, for which novel and efficient solutions are provided.The focus of this thesis is on the following underlying challenges. First, the estimation of the 3D room layout from a single 360º image is investigated, which is used for the highest level of scene modelling and understanding. We exploit the assumption of Manhattan World and deep learning techniques to propose models that handle invisible parts of the room on the image, generalizing to more complex layouts. At the same time, new methods to work with 360º images are proposed, highlighting a special convolution that compensates the equirectangular image distortions.Second, considering the importance of context for scene understanding, we study the problem of object localization and segmentation, adapting the problem to leverage 360º images. We also exploit layout-objects interaction to lift detected 2D objects into the 3D room model.The final line of work of this thesis focuses on 3D object shape analysis. We use an explicit modelling of non-rigidity and a high-level notion of object symmetry to learn, in an unsupervised manner, 3D keypoints that are order-wise correspondent as well as geometrically and semantically consistent across objects in a category.Our models advance state-of-the-art on the aforementioned tasks, when each evaluated on respective reference benchmarks
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Xiao, Zhuoling. "Robust indoor positioning with lifelong learning." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:218283f1-e28a-4ad0-9637-e2acd67ec394.

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Indoor tracking and navigation is a fundamental need for pervasive and context-aware applications. However, no practical and reliable indoor positioning solution is available at present. The major challenge of a practical solution lies in the fact that only the existing devices and infrastructure can be utilized to achieve high positioning accuracy. This thesis presents a robust indoor positioning system with the lifelong learning ability. The typical features of the proposed solution is low-cost, accurate, robust, and scalable. This system only takes the floor plan and the existing devices, e.g. phones, pads, etc. and infrastructure such as WiFi/BLE access points for the sake of practicality. This system has four closely correlated components including, non-line-of-sight identification and mitigation (NIMIT), robust pedestrian dead reckoning (R-PDR), lightweight map matching (MapCraft), and lifelong learning. NIMIT projects the received signal strength (RSS) from WiFi/BLE to locations. The R-PDR component converts the data from inertial measurement unit (IMU) sensors ubiquitous in mobile devices and wearables to the trajectories of the user. Then MapCraft fuses trajectories estimated from the R-PDR and the coarse location information from NIMIT with the floor plan and provides accurate location estimations. The lifelong learning component then learns the various parameters used in all other three components in an unsupervised manner, which continuously improves the the positioning accuracy of the system. Extensive real world experiments in multiple sites show how the proposed system outperforms state-of-the art approaches, demonstrating excellent sub-meter positioning accuracy and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position regardless of the users, devices, attachments, and environments. We believe that such an accurate and robust approach will enable always-on background localization, enabling a new era of location-aware applications to be developed.
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Selin, Magnus. "Efficient Autonomous Exploration Planning of Large-Scale 3D-Environments : A tool for autonomous 3D exploration indoor." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163329.

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Exploration is of interest for autonomous mapping and rescue applications using unmanned vehicles. The objective is to, without any prior information, explore all initially unmapped space. We present a system that can perform fast and efficient exploration of large scale arbitrary 3D environments. We combine frontier exploration planning (FEP) as a global planning strategy, together with receding horizon planning (RH-NBVP) for local planning. This leads to plans that incorporate information gain along the way, but do not get stuck in already explored regions. Furthermore, we make the potential information gain estimation more efficient, through sparse ray-tracing, and caching of already estimated gains. The worked carried out in this thesis has been published as a paper in Robotand Automation letters and presented at the International Conference on Robotics and Automation in Montreal 2019.
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Книги з теми "Indoor robotics"

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M, Evans J., and National Institute of Standards and Technology (U.S.), eds. Three dimensional data capture in indoor environments for autonomous navigation. Gaithersburg, Md: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.

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1942-, Evans John M., and National Institute of Standards and Technology (U.S.), eds. Three dimensional data capture in indoor environments for autonomous navigation. Gaithersburg, Md: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.

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3

Florczyk, Stefan. Robot Vision: Video-Based Indoor Exploration with Autonomous and Mobile Robots. Wiley & Sons, Incorporated, John, 2006.

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FLORCZYK, STEFAN. Robot Vision: Video-Based Indoor Exploration with Autonomous and Mobile Robots. Not Avail, 2005.

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5

Sanfeliu, Alberto, and Juan Andrade Cetto. Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building. Springer, 2010.

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6

Andrade-Cetto, Juan, and Alberto Sanfeliu. Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building (Springer Tracts in Advanced Robotics). Springer, 2006.

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7

Zufferey, Jean-Christophe. Bio-Inspired Flying Robots: Experimental Synthesis of Autonomous Indoor Flyers. Taylor & Francis Group, 2008.

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8

Environment Learning for Indoor Mobile Robots. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/11418382.

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Andrade-Cetto, Alberto Sanfeliu Juan. Environment Learning for Indoor Mobile Robots. Springer, 2009.

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10

Zufferey, Jean-Christophe. Bio-Inspired Flying Robots: Experimental Synthesis of Autonomous Indoor Flyers. Taylor & Francis Group, 2008.

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Частини книг з теми "Indoor robotics"

1

Zhang, Rui, Wanyue Jiang, Zhonghao Zhang, Yuhan Zheng, and Shuzhi Sam Ge. "Indoor Mobile Robot Socially Concomitant Navigation System." In Social Robotics, 485–95. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24667-8_43.

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2

Ah Sen, Nick, Pamela Carreno-Medrano, and Dana Kulić. "Human-Aware Subgoal Generation in Crowded Indoor Environments." In Social Robotics, 50–60. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24667-8_5.

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3

Sprunk, Christoph, Jörg Röwekämper, Gershon Parent, Luciano Spinello, Gian Diego Tipaldi, Wolfram Burgard, and Mihai Jalobeanu. "An Experimental Protocol for Benchmarking Robotic Indoor Navigation." In Experimental Robotics, 487–504. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23778-7_32.

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4

Liu, Zhongzheng, Zhigang Liu, and Bingheng Lu. "Error Compensation of Indoor GPS Measurement." In Intelligent Robotics and Applications, 612–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88518-4_66.

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Zhu, Huishen, Brenton Leighton, Yongbo Chen, Xijun Ke, Songtao Liu, and Liang Zhao. "Indoor Navigation System Using the Fetch Robot." In Intelligent Robotics and Applications, 686–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27538-9_59.

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Shen, Shaojie, and Nathan Michael. "State Estimation for Indoor and Outdoor Operation with a Micro-Aerial Vehicle." In Experimental Robotics, 273–88. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00065-7_20.

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Yang, Dongjun, and Younggoo Kwon. "The Integrated Indoor Positioning by Considering Spatial Characteristics." In Intelligent Robotics and Applications, 246–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65298-6_23.

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Henry, Peter, Michael Krainin, Evan Herbst, Xiaofeng Ren, and Dieter Fox. "RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments." In Experimental Robotics, 477–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-28572-1_33.

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Urcola, P., M. T. Lorente, J. L. Villarroel, and L. Montano. "Seamless Indoor-Outdoor Robust Localization for Robots." In ROBOT2013: First Iberian Robotics Conference, 275–87. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03653-3_21.

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Brooks, Alex, Alexei Makarenko, Tobias Kaupp, Stefan Williams, and Hugh Durrant-Whyte. "Implementation of an Indoor Active Sensor Network." In Springer Tracts in Advanced Robotics, 397–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11552246_38.

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Тези доповідей конференцій з теми "Indoor robotics"

1

Dias, André, João J. Martins, José Antunes, André Moura, and José Almeida. "MANTIS: UAV for Indoor Logistic Operations." In 2024 7th Iberian Robotics Conference (ROBOT), 1–7. IEEE, 2024. https://doi.org/10.1109/robot61475.2024.10796935.

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2

Sheng, Diwei, Anbang Yang, John-Ross Rizzo, and Chen Feng. "NYC-Indoor-VPR: A Long-Term Indoor Visual Place Recognition Dataset with Semi-Automatic Annotation." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 14853–59. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610564.

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Martins, João J., Alexandre Amaral, and André Dias. "Deep Reinforcement Learning Framework for UAV Indoor Navigation." In 2024 7th Iberian Robotics Conference (ROBOT), 1–8. IEEE, 2024. https://doi.org/10.1109/robot61475.2024.10796906.

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Yi, Zhaoguang, Xiangyu Wen, Qiyue Xia, Peize Li, Francisco Zampella, Firas Alsehly, and Chris Xiaoxuan Lu. "Multimodal Indoor Localization Using Crowdsourced Radio Maps." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 13666–72. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610683.

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Jiang, Fan, David Caruso, Ashutosh Dhekne, Qi Qu, Jakob Julian Engel, and Jing Dong. "Robust Indoor Localization with Ranging-IMU Fusion." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 11963–69. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611274.

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Qi, Josiah Yang, Lai Chean Hung, Hudyjaya Siswoyo Jo, Justin Sia Wei Kiat, Evon Lim Wan Ting, and Michelle Dunn. "A Review of Sustainable Farming with Robotics in Indoor Greenhouse Environments." In 2024 IEEE 12th Region 10 Humanitarian Technology Conference (R10-HTC), 1–6. IEEE, 2024. https://doi.org/10.1109/r10-htc59322.2024.10778747.

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Shu, Manli, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, and Ran Xu. "Hierarchical Point Attention for Indoor 3D Object Detection." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 4245–51. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610108.

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Kwon, Obin, Dongki Jung, Youngji Kim, Soohyun Ryu, Suyong Yeon, Songhwai Oh, and Donghwan Lee. "WayIL: Image-based Indoor Localization with Wayfinding Maps." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 6274–81. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610480.

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9

Zeng, Jing, Yanxu Li, Jiahao Sun, Qi Ye, Yunlong Ran, and Jiming Chen. "Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 18041–47. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611382.

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10

Tai, Beijia, Yangjie Wei, Guangyu Zhang, Yuqing He, and Siyao Cui. "Indoor Multi-Anchor Collaborative Localization for Unmanned Systems." In 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1036–41. IEEE, 2024. https://doi.org/10.1109/robio64047.2024.10907434.

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Звіти організацій з теми "Indoor robotics"

1

Mekonnen, Bisrat, Benjamin Christie, Michael Paquette, and Garry Glaspell. 3D mapping and navigation using MOVEit. Engineer Research and Development Center (U.S.), June 2023. http://dx.doi.org/10.21079/11681/47179.

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Анотація:
Until recently, our focus has been primarily on the development of a low SWAP-C payload for deployment on a UGV that leverages 2D mapping and navigation. Due to these efforts, we are able to autonomously map and navigate very well within flat indoor environments. This report will explore the implementation of 3D mapping and navigation to allow unmanned vehicles to operate on a variety of terrains, both indoor and outdoor. The method we followed uses MOVEit, a motion planning framework. The MOVEit application is typically used in the control of robotic arms or manipulators, but its handling of 3D perception using OctoMaps makes it a promising software for robots in general. The challenges of using MOVEit outside of its intended use case of manipulators are discussed in this report.
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