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The video shows a very high resolution 3D point cloud !!! of the outdoor area of the German Rescue Robotics Center. For the recording, a 25-second POI flight was performed with a Mavic 3. From the 4K video footage captured during this flight, 77 images were cropped and localized within 4 minutes using colmap and processed using Neural Radiance Fields (NeRF). The nerfacto model of Nerfstudio was trained on an Nvidia RTX 4090 for 8 minutes. In summary, a top 3D model is available to task forces after about 13 minutes. The calculation is performed locally on site by the RobLW of the DRZ. The video shown here shows a free camera path rendered at 60 hz (Full HD).
Nerf(acto) for the 3D modeling of the Computer Science building of Westfälische Hochschule GE
(2023)
The video shows a very high resolution 3D point cloud !!! of the computer science building of the University of Applied Science Gelsenkirchen. For the recording a 3 minute flight with a M30T was performed. The 105 images taken by the wide-angle camera during this flight were localized within 3 minutes using colmap and processed using Neural Radiance Fields (NeRF). The nerfacto model of Nerfstudio was trained on an Nvidia RTX 4090 for 8 minutes. Thus, a top 3D model is available after about 15 minutes.
The video shown here shows a free camera path rendered at 60 hz (Full HD).
From the 360° images of the former video (
• German rescue robotic center captured... ) we now generate the 3D point cloud. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The 3D point cloud generation is 5x slower than the video. It uses a VSLAM algorithm to localize the k-frames (green) and with 3 k-frames it use a 360° PatchMatch algorithm implemented at a NVIDIA graphic card (CUDA) to calculated the dense point clouds.The hall ist about 70 x 20 meters.
The video shows the first test of a small spherical UAV (35 cm) with 4 rotors for missions in complex environments such as buildings, caves or tunnels. The spherical design protects the vehicle's internal components and allows the UAV to roll over the ground when the environment allows. The drone can land and take off in any position and come into contact with objects without endangering the propellers and can restart even after crashes.
Sperical UAV: Crash Test with 1/2 liter bottle from 2 meters
Gaussian Splatting: 3D Reconstruction of a Chemical Company After a Tank Explosion in Kempen 8/2023
(2023)
The video showcases a 3D model of a chemical company following a tank explosion that occurred on August 17, 2023, in Kempen computed with the gaussian splatting algorithm. Captured by a compact mini drone measuring 18cm x 18cm and equipped with a 360° camera, these images offer an intricate perspective of the aftermath. The computation need 29 minutes and uses 2770 images (~350 equirectangular images). After a comprehensive aerial survey and inspection of the 360° images taken within the facility, authorities confirmed that it was safe for the evacuated residents to return to their homes. See also:
https://www1.wdr.de/fernsehen/aktuelle-stunde/alle-videos/video-grosser-chemieunfall-in-kempen-100.html
The video showcases a 3D model of a chemical company following a tank explosion that occurred on August 17, 2023, in Kempen computed with the AI algorithm Neural Radiance Field (NeRF). Captured by a compact mini drone measuring 18cm x 18cm and equipped with a 360° camera, these images offer an intricate perspective of the aftermath. After a comprehensive aerial survey and inspection of the 360° images taken within the facility, authorities confirmed that it was safe for the evacuated residents to return to their homes. See also:
https://www1.wdr.de/fernsehen/aktuelle-stunde/alle-videos/video-grosser-chemieunfall-in-kempen-100.html
ARGUS is a tool for the systematic acquisition, documentation and evaluation of drone flights in rescue operations. In addition to the very fast generation of RGB and IR orthophotos, a trained AI can automatically detect fire, people and cars in the images captured by the drones. The video gives a short introduction to the Aerial Rescue and Geospatial Utility System -- ARGUS
Check out our Github repository under
https://github.com/RoblabWh/argus/
You can find the dataset on kaggle under
https://www.kaggle.com/datasets/julienmeine/rescue-object-detection
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.
360° and IR- Camera Drone Flight Test: Superimposition of two data sources for Post-Fire Inspection
(2023)
This video highlights a recent flight test carried out in our cutting-edge robotics lab, unveiling the capabilities of our meticulously crafted thermal and 360° camera drone! We've ingeniously upgraded a DJI Avata with a bespoke thermal and 360° camera system. Compact yet powerful, measuring just 18 x 18 x 17 cm, this drone is strategically engineered to effortlessly navigate and deliver crucial thermal and 360° insights concurrently in post-fire or post-explosion environments.
The integration of a specialized thermal and 360° camera system enables the simultaneous capture of both data sources during a single flight. This groundbreaking approach not only reduces inspection time by half but also facilitates the seamless superimposition of thermal and 360° videos for comprehensive analysis and interpretation.
At the integration sprint of the E-DRZ consortium in march 2023 we improve the information captured by the human spotter (of the fire brigade) by extending him through a 360° drone i.e. the DJI Avata with an Insta360 on top of it. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The hall ist about 70 x 20 meters. When the drone is landed we have all information in 360° degree at 5.7k as you can see it in the video. Furthermore it is a perfect documentation of the deployment scenario. In the next video we will show how to spatial localize the 360° video and how to generate a 3D point cloud from it.
At the integration sprint of the E-DRZ consortium in march 2023 we improve the information captured by the human spotter (of the fire brigade) by extending him through a 360° drone. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The hall ist about 70 x 20 meters. When the drone is landed we have all information in 360° degree at 5.7k as you can see it in the video. Furthermore it is a perfect documentation of the deployment scenario. In the next video we will show how to spatial localize the 360° video and how to generate a 3D point cloud from it.
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.
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.
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.
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.
360° UAV Flight in a collapse test setup at the German Resuce Robotik Center
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.