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- Informatik und Kommunikation (60) (entfernen)
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.
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.
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.
This video features a flight test conducted in our robotics lab, showcasing a custom-built thermal camera drone. We've enhanced a DJI Avata with a specialized thermal camera system. With its compact dimensions measuring 18 x 18 x 17 cm, this drone is designed to navigate and provide critical thermal information within post-fire or post-explosion environments. For more insights, be sure to check out our previous videos on this channel.
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.