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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).
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.
This technical report is about the architecture and integration of commercial UAVs in Search and Rescue missions. We describe a framework that consists of heterogeneous UAVs, a UAV task planner, a bridge to the UAVs, an intelligent image hub, and a 3D point cloud generator. A first version of the framework was developed and tested in several training missions in the EU project TRADR.
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.
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.
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
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.
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.
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.