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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.
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 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.
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.