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Institut
Durch den digitalen Wandel haben Kommunen neue Entwicklungsmöglichkeiten im Bereich der Smart City. Diese Arbeit stellt eine Übersicht darüber dar, wie mithilfe von IoT, Big Data, Datenbanken, Digitalen Zwillingen und weiteren Technologien, eine Mikroklima-Analyse und Steuerung ermöglicht werden kann.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
Desert ants Cataglyphis spec. monitor inclination and distance covered through force-based sensing in their legs. To transfer this mechanism to legged robots, artificial neural networks are used to determine the inclination angle of an experimental ramp from the motor data of the legs of a commercial hexapod walking robot. It is possible to determine the inclination angle of the ramp based on the motor data of the robot legs read out during a run. The result is independent of the weight and orientation of the robot on the ramp and hence robust enough to serve as an independent odometer.
Biomechanische Untersuchungen zum Öffnungsmechanismus von verholzten Früchten der Gattung Hakea
(2023)
Die Arten H. sericea und H. salicifolia (Proteaceae) sind in Australien heimisch. Ihr natürlicher Lebensraum ist trocken und nährstoffarm, und sie sind regelmäßig Buschbränden ausgesetzt. Durch den Feuchtigkeitsverlust “schrumpft“ die Frucht und öffnet sich, wobei zwei Samen freigesetzt werden. Diese Arbeit vergleicht das Öffnungsverhalten von manipulierten Früchten, das Schwindmaß, die Öffnungskraft, den Elastizitätsmodul und die Druckfestigkeit der beiden Arten und untersucht den Einfluss verschiedener Gewebe auf die Öffnung. Es wird festgestellt, dass das Mesokarp hauptsächlich für das anisotrope Schwindverhalten verantwortlich ist.
This paper reveals various approaches undertaken over more than two decades of teaching undergraduate programming classes at different Higher Education Institutions, in order to improve student activation and participation in class and consequently teaching and learning effectiveness.
While new technologies and the ubiquity of smartphones and internet access has brought new tools to the classroom and opened new didactic approaches, lessons learned from this personal long-term study show that neither technology itself nor any single new and often hyped didactic approach ensured sustained improvement of student activation. Rather it needs an integrated yet open approach towards a participative learning space supported but not created by new tools, technology and innovative teaching methods.
This paper presents a pragmatic approach for stepwise introduction of peer assessment elements in undergraduate programming classes, discusses some lessons learned so far and directions for further work. Students are invited to challenge their peers with their own programming exercises to be submitted through Moodle and evaluated by other students according to a predefined rubric and supervised by teaching assistants. Preliminary results show an increased activation and motivation of students leading to a better performance in the final programming exams.
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.