Informatik und Kommunikation
Filtern
Erscheinungsjahr
Dokumenttyp
- Video (55)
- Konferenzveröffentlichung (14)
- Wissenschaftlicher Artikel (12)
- Teil eines Buches (Kapitel) (8)
- Vorlesung (7)
- Masterarbeit (2)
- Bericht (2)
- Sonstiges (1)
- Arbeitspapier (1)
Volltext vorhanden
- ja (102) (entfernen)
Schlagworte
- Robotik (27)
- Flugkörper (18)
- UAV (18)
- 3D Modell (7)
- Akkreditierung (6)
- E-Learning (6)
- Radio-Feature (6)
- Rettungsrobotik (5)
- Virtuelle Hochschule (5)
- Rasenmäher (4)
The dataset is used for 3D environment modeling, i.e. for the generation of dense 3D point clouds and 3D models with PatchMatch algorithm and neural networks. Difficult for the modeling algorithm are the reflections of rain, water and snow, as well as windows and vehicle surface. In addition, lighting conditions are constantly changing.
Hintergrund
Während der SARS-CoV-2-Pandemie ist es vorrangig, die Mitarbeiter vor Infektionsrisiken zu schützen und die Geschäftstätigkeit zu sichern. Neue Virusvarianten mit erhöhter Ansteckungsgefahr erfordern eine weiterentwickelte Risikostrategie.
Material und Methoden
Mehrere Standardmaßnahmen wie Tests, Isolierung und Quarantäne werden zu einer neuartigen Risikostrategie kombiniert. Epidemiologische Modellrechnungen und wissenschaftliche Erkenntnisse über den Verlauf der SARS-CoV-2-Infektiosität werden zur Optimierung dieser Strategie herangezogen. Das Verfahren ist in einem einfach zu bedienenden Rechner auf Excel-Basis implementiert.
Aufbau in der Praxis und Ergebnisse
Alternative Maßnahmenkombinationen und praktische Aspekte werden erörtert. Anhand von Beispielrechnungen wird die Wirkung der diskutierten Maßnahmen demonstriert.
Schlussfolgerung
Der aus diesen Grundlagen abgeleitete Quarantäne-Rechner ermöglicht es auch Nicht-Fachleuten, eine differenzierte Risikoanalyse durchzuführen und optimierte Maßnahmen einzuleiten. Gezielte Prüfroutinen und alternative Maßnahmen sichern die Personalverfügbarkeit.
Körperliche Behinderungen können einen Menschen soweit einschränken, dass für sie ein autonomes und selbstbestimmtes Leben, trotz intakter mentaler und kognitiven Fähigkeiten, nicht mehr möglich ist. Daher ist für Menschen, die beispielsweise vom Hals abwärts gelähmt sind, sogenannten Tetraplegikern, jede Zurückgewinnung von Autonomie eine Steigerung der Lebensqualität. In dieser Masterarbeit wird ein Augmented Reality Prototyp entwickelt, der es Tetraplegikern oder Menschen mit einer ähnlichen körperlichen Einschränkung erlaubt, an einem Mensch-Roboter Arbeitsplatz Montageaufgaben durchzuführen und ihnen somit eine Integration ins Arbeitsleben ermöglichen kann. Der Prototyp erlaubt es den Benutzer ohne die Nutzung der Hände, einen Kuka iiwa Roboterarm mit der Microsoft HoloLens zu steuern. Dabei wird ein Fokus darauf gelegt, das Blickfeld des Benutzers mit speziellen virtuellen Visualisierungen, sogenannten visuellen Helfern, anzureichern, um die Nachteile, die durch die Bewegungseinschränkungen der Zielgruppe ausgelöst werden, auszugleichen. Diese visuellen Helfer sollen bei der Steuerung des Roboterarms unterstützen und die Bedienung des Prototyps verbessern. Eine Evaluation des Prototyps zeigte Tendenzen, dass das Konzept der visuellen Helfer den Benutzer den Roboterarm präziser steuern lässt und seine Bedienung unterstützt.
The two churches, San Francesco and Sant'Agostino in Amatrice, Italy was hit by an earthquake on August 24 2016. Both churches are in a state of partial collapse, in need of shoring to prevent potential further destruction and to preserve the national heritage. The video show the mission at 1.Sept.2016 in clips of 10 seconds.
The TRADR project was asked by the Italian firebrigade Vigili del Fuoco to provide 3D textured models of two churches.
The team entered San Francesco with two UGVs (ground robots) and one UAV (drone, flown by Prof. Surmann), teleoperating them entirely out of line of sight and partially in collaboration. We entered Sant'Agostino with one UAV (also flown by Prof. Surmann) while two other UAVs were providing a view from different angles to facilitate maneuvering them entirely out of line of sight.
Venice 2018: Tradr Review
(2018)
The video shows an orthopoto and a textured 3D model of the location. 300 images were recorded in two short flights with a Mavic Pro in 50 meter height. The first one was a single grid while the camera facing down and the second one was a double grid facing the camera at an 60 degree angle. The 3D model is computed with OpenDroneMap.
This video shows a model computed from 124 images taken at the Tjex 2015 of the trade project (www.tradr-project.eu). The images were acquired by walking around the object and reconstruct the structure with VisualSfm software.
The video shows a snapshot of a 16 minute flight of a DJI Phantom 3 professional over the Schloss Birlinghoven at Sankt Augustin, Germany. The castle is located at the Fraunhofer Campus at Sankt Augustin. The 3D model is generated out of 400 key frames of the 4k video which are cut out with ffmpeg. The work is part of an evaluation in the Tradr Project (www.tradr-project.eu)
Challenging visual localization of an UAV while flying out of a room into a snowy environment (~ 4:50). The UAV is equipped with a 360° camera. The localization is done with OpenVSLAM.
The video was recorded in Jan. 2019 at the Fire Brigade training center in Dortmund
To achieve nearly real time conditions the original resolution of 5k (30 fps) was reduced to 2k (ffmpeg -i video.mp4 -vf scale=1920:-1 -crf 25 vido-small.mp4) with high compression (-crf 25). This reduce the original size from 3.2 GB to 93MB (~ 4 MBit/s which could be transmitted online via a radio link). The localization shown did not use frameskip. With a frameskip above 1 the localization fails while the UAV is flying through the window. Indoor localization can be done with a frameskip of 3 in real time.
9 Panoramen, das erste ist aus größerer Höhe aufgenommen und enthält im Himmel eine Karte mit den Positionen der aufgenommenen Punkte (gelb). Das aktuelle Bild ist im Fadenkreuz (rot). Zusätzlich noch ein paar Details zu dem aktuellen Punkt. Jedes Panorama ist 10 Sekunden lang.
Zum Betrachten die höchste Auflösungsstufe wählen und die Pausetaste verwenden. Mit dem gedrückten linken Button kann man sich im Bild bewegen.
At the beginning of the pandemic in Feb. 2020 I had a little time and wanted to do something new i.e. bring my 3D printer, AI and computer science together somehow. The result is a printed portrait with a lot of computer science. Using style transfer I transferred the etching style of a Göthe portrait to a young girl I call Carolin. By means of image processing I made a black and white picture out of it. Then, using the problem of the traveling salesman, each black point in the picture is interpreted as a city and the whole picture is drawn by only one line. Since this line is very long, it is optimized and shortened by a so-called simulated annealing algorithm. The result is printed in 5 layers on a 3D printer.
360° Camera at a small UAV
(2021)
Durch Panoramen in Kombination mit dem ORB-SLAM ist ein schnelles Tracking möglich, liefert jedoch ausschließlich spärliche Daten. Durch die Kombination mit einem neuronalen Netz soll der SLAM Algorithmus zu einem RGBD-SLAM erweitert werden, um ein besseres Tracking und eine dichtere Punktwolke zu gewährleisten.
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
This technical report is about the architecture and integration of very small commercial UAVs (< 40 cm diagonal) in indoor Search and Rescue missions. One UAV is manually controlled by only one single human operator delivering live video streams and image series for later 3D scene modelling and inspection. In order to assist the operator who has to simultaneously observe the environment and navigate through it we use multiple deep neural networks to provide guided autonomy, automatic object detection and classification and local 3D scene modelling. Our methods help to reduce the cognitive load of the operator. We describe a framework for quick integration of new methods from the field of Deep Learning, enabling for rapid evaluation in real scenarios, including the interaction of methods.
Wie können mit Luftbildaufnahmen 3D Modelle generiert werden?
- Planen von kreisförmigen und einen rasterförmigen Flug Trajektorien.
- Autonomes Abfliegen und Aufnahme der Bilder
- Verortung der Bilder mittels GPS und Structure from Motion Algorithmen.
- Generierung von 3D Modellen mithilfe von Multi-View Stereo Algorithmen.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
This technical report is about the mission and the experience gained during the reconnaissance of an industrial hall with hazardous substances after a major fire in Berlin. During this operation, only UAVs and cameras were used to obtain information about the site and the building. First, a geo-referenced 3D model of the building was created in order to plan the entry into the hall. Subsequently, the UAVs were used to fly in the heavily damaged interior and take pictures from inside of the hall. A 360° camera mounted under the UAV was used to collect images of the surrounding area especially from sections that were difficult to fly into. Since the collected data set contained similar images as well as blurred images, it was cleaned from non-optimal images using visual SLAM, bundle adjustment and blur detection so that a 3D model and overviews could be calculated. It was shown that the emergency services were not able to extract the necessary information from the 3D model. Therefore, an interactive panorama viewer with links to other 360° images was implemented where the links to the other images depends on the semi dense point cloud and located camera positions of the visual SLAM algorithm so that the emergency forces could view the surroundings.