Informatik und Kommunikation
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.
The disruptive nature of the changing media landscape and technology-driven advances in communication have led to innovative ways of organizing work in the information and communication industry. This reorganization of work is reflected in the concept of New Work, which rethinks working concepts, styles, and employee behavior. Based on a survey among staff in the information and communication industry (n = 380), this study investigates the status quo of the implementation of New Work measures and their effectiveness in helping companies reach organizational goals. The results show that New Work measures are widely adopted although there is still unused potential. Moreover, the study demonstrates that the implementation of New Work measures supports companies in achieving New Work goals as well as overall organizational goals in the contexts of agile management, change management, internal communication, and evaluation.
Sperical UAV: Crash Test with 1/2 liter bottle from 2 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.
Problem: A group of robots, called a swarm, is placed in an unknown environment and is supposed to explore it independently. The goal of the exploration is the creation of a common map.
Implementation
- Equipping six Kobuki robots with appropriate sensor technology, a large battery, a router and the Jetson board
- Setup of the Jetson-Boards with self-made ROS2 nodes and the set up mesh network
- Writing of launch files for the common start of all functions
- Reinforcement learning is used to train an AI that controls the swarm by selecting points for the robots to approach and navigating to them and navigating them there.
- Setting up a responsive website using Angular and the Bootstrap
Framework.
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.
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
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).