3D rekonstrukce na iOS

This bachelor thesis describes implementation of a real-time RGBD-based 3D reconstruction pipeline suited for Apple’s iPhone X with the TrueDepth camera. First, an overview of common approaches to the reconstruction problem is made, followed by a description of the underlying algorithms and techniques used in the thesis. Finally, the implementation details of the application pipeline are presented with performance overview of the implemented application.Tato bakalářská práce popisuje implementaci řetězce pro 3D rekonstrukci z RGBD snímků v reálném čase, určené pro Apple iPhone X s TrueDepth kamerou. Nejdříve je podán přehled běžných přístupů k rekonstrukci, následován popisem algoritmů a technik použitých v této práci. Nakonec jsou popsány implementační detaily zvoleného rekonstrukčního řetězce spolu s popisem výkonnosti implementované aplikace.460 - Katedra informatikyvýborn

Dense Visual Simultaneous Localisation and Mapping in Collaborative and Outdoor Scenarios

Dense visual simultaneous localisation and mapping (SLAM) systems can produce 3D reconstructions that are digital facsimiles of the physical space they describe. Systems that can produce dense maps with this level of fidelity in real time provide foundational spatial reasoning capabilities for many downstream tasks in autonomous robotics. Over the past 15 years, mapping small scale, indoor environments, such as desks and buildings, with a single slow moving, hand-held sensor has been one of the central focuses of dense visual SLAM research. However, most dense visual SLAM systems exhibit a number of limitations which mean they cannot be directly applied in collaborative or outdoors settings. The contribution of this thesis is to address these limitations with the development of new systems and algorithms for collaborative dense mapping, efficient dense alternation and outdoors operation with fast camera motion and wide field of view (FOV) cameras. We use ElasticFusion, a state-of-the-art dense SLAM system, as our starting point where each of these contributions is implemented as a novel extension to the system. We first present a collaborative dense SLAM system that allows a number of cameras starting with unknown initial relative positions to maintain local maps with the original ElasticFusion algorithm. Visual place recognition across local maps results in constraints that allow maps to be aligned into a common global reference frame, facilitating collaborative mapping and tracking of multiple cameras within a shared map. Within dense alternation based SLAM systems, the standard approach is to fuse every frame into the dense model without considering whether the information contained within the frame is already captured by the dense map and therefore redundant. As the number of cameras or the scale of the map increases, this approach becomes inefficient. In our second contribution, we address this inefficiency by introducing a novel information theoretic approach to keyframe selection that allows the system to avoid processing redundant information. We implement the procedure within ElasticFusion, demonstrating a marked reduction in the number of frames required by the system to estimate an accurate, denoised surface reconstruction. Before dense SLAM techniques can be applied in outdoor scenarios we must first address their reliance on active depth cameras, and their lack of suitability to fast camera motion. In our third contribution we present an outdoor dense SLAM system. The system overcomes the need for an active sensor by employing neural network-based depth inference to predict the geometry of the scene as it appears in each image. To address the issue of camera tracking during fast motion we employ a hybrid architecture, combining elements of both dense and sparse SLAM systems to perform camera tracking and to achieve globally consistent dense mapping. Automotive applications present a particularly important setting for dense visual SLAM systems. Such applications are characterised by their use of wide FOV cameras and are therefore not accurately modelled by the standard pinhole camera model. The fourth contribution of this thesis is to extend the above hybrid sparse-dense monocular SLAM system to cater for large FOV fisheye imagery. This is achieved by reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. To estimate depth, we introduce a new version of the PackNet depth estimation neural network (Guizilini et al., 2020) adapted for fisheye inputs. To demonstrate the effectiveness of our contributions, we present experimental results, computed by processing the synthetic ICL-NUIM dataset of Handa et al. (2014) as well as the real-world TUM-RGBD dataset of Sturm et al. (2012). For outdoor SLAM we show the results of our system processing the autonomous driving KITTI and KITTI-360 datasets of Geiger et al. (2012a) and Liao et al. (2021) respectively

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Multibody dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: Formulations and Numerical Methods, Efficient Methods and Real-Time Applications, Flexible Multibody Dynamics, Contact Dynamics and Constraints, Multiphysics and Coupled Problems, Control and Optimization, Software Development and Computer Technology, Aerospace and Maritime Applications, Biomechanics, Railroad Vehicle Dynamics, Road Vehicle Dynamics, Robotics, Benchmark Problems. The conference is organized by the Department of Mechanical Engineering of the Universitat Politècnica de Catalunya (UPC) in Barcelona. The organizers would like to thank the authors for submitting their contributions, the keynote lecturers for accepting the invitation and for the quality of their talks, the awards and scientific committees for their support to the organization of the conference, and finally the topic organizers for reviewing all extended abstracts and selecting the awards nominees.Postprint (published version

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed