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- Video (60) (entfernen)
Schlagworte
- Robotik (29)
- Flugkörper (21)
- UAV (21)
- Rettungsrobotik (8)
- 3D Modell (7)
- Rasenmäher (4)
- DRZ (3)
- Deutsches Rettungsrobotik-Zentrum (3)
- SLAM (3)
- Kartierung (2)
Problem
- How to effectively use aerial robots to support rescue forces?
- How to achieve good flight characteristics and long flight times?
- How to enable simple and intuitive control?
- How to efficiently record image data of the environment?
- How to generate flight and image data for rescue forces?
Implementation:
The flying robot was designed in Autodesk Fusion360. In order to achieve high stability as well as low weight, the frame was milled from carbon. Mounts such as for GPS and 360° camera were 3D printed. A special feature is that the flying robot is not visible in the panoramic view of the 360° camera. The flight controller of the robot was set up using Ardupilot. The communication with the robot is done via MAVLink (UDP).To support different platforms, a software was realized as a web application. The front end was created using HTML, CSS and Javascript.
The back end is based on Flask-Socket-IO (Python). For the intelligent recognition of motor vehicles a micro controller with an integrated camera is used. For the post-processing of flight and video data a pipeline was implemented for automation.
360° UAV Flight in a collapse test setup at the German Resuce Robotik Center
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.
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.
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.
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.
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.
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.
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.
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)
This video shows a model computed from 320 images taken at the Tjex 2015 of the trade project (www.tradr-project.eu). The images were acquired with a falcan 8 drone (AscTec) and reconstruct the structure with VisualSfm software. The flight was in 150 m. The Tower is about 95 meter high.
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.