Scientific Projects

Recent disaster events, like the 2021 flood in Germany showed clearly, that even the best alert systems and top first responder organisations can not prevent fatalities and serious damage on property without having prepared the citizens how to act and react during disaster situations and crises, understand alerts and follow instructions. B-prepared offers a cost-effective solution for building a culture of disaster preparedness with a multi-actor approach in realistic historical scenarios.

With technology rapidly enhancing and becoming more affordable, commercial and consumer drones have become increasingly popular since the beginning of the 21st century. They pose serious threats to safety (e.g. disturbances for airports and airplanes) and privacy (recording without consent, etc) as well. It is evident that in order to mitigate these issues and increase security it is critical to develop solutions that incorporate the detection and tracking of UAVs with autonomous systems.

Higher education has to keep pace with the global market needs for the necessary ICT(Information and Communications Technology) skills and the overall understanding of the complexity of industries in the 21st century. Global market companies have to effectively deal with the constant evolution of products, processes and production systems (and all in parallel) that can be more easily monitored, developed and up-graded using digital applications based on the concept of digital twin and taking advantage of Virtual Reality (VR) and Augmented Reality (AR) simulations. 

Numerous automotive and small aircraft companies have announced promising new applications in the field of autonomous vehicles. Alongside self-driving cars, in the near future small-size micro aerial vehicles could be used for goods delivery (Amazon Prime Air, DHL, Alibaba, Matternet, Swiss Post), in healthcare (Matternet, Flirtey, Wingtra, RedLine), to carry out various inspection and surveillance tasks (SenseFly, Skycatch), or can be deployed at accidents as remote-controlled first aid/responder devices (Drone Aventures, Microdrones).

The project aims to process the data of novel 3D sensors (e.g. Microsoft Kinect, Lidar, MRI, CT) available in a wide range of application fields and to fuse them with 2D image modalities to build saliency models, which are able to automatically and efficiently emphasize visually dominant regions. Such models not only tighten the region of interest for further image processing steps, but facilitate and increase the efficiency of segmentation in different application fields with available 3D sensor data, e.g.

The aim of the project is to develop an image fusion and processing method that uses images of cameras with different modalities to track various objects, taking into account the needs of border su

Various key aspect of machine-based environment interpretation are the automatic detection and recognition of objects, obstacle avoidance in navigation, and object tracking in certain applications. Integrating visual sensors, such as video cameras, with sensors providing direct 3D spatial measurements, such as Lidars may offer various benefits (high spatial and temporal resolution, distance, color or illumination invariance). However, fusing the different data modalities often implies sensor specific unique challenges.

In this project we address a new and very important issue: the observation of small backcountry wetland areas surrounded by different areas, hosting important species and delivering essential ecosystem services and biodiversity. Although these patches are small one by one, but together they can contribute to the wetland cover area with a very high rate – their protection and mapping is a need.

Recent Simultaneous Localization and Mapping (SLAM) algorithms are basically developed for stable environment in time; dynamic scenes cause strong bias in the localization models. For this reason we will improve the conventional SLAM calculus with statistical optimizing the models of changing parts and their neighborhood connection; this will result in semantic connectedness investigation on the models, which needs good classification methods of the scalable cluster structure. 

Up to date 3D sensors revolutionized the acquisition of environmental information. 3D vision systems of self driving vehicles can be used for -apart from safe navigation- real time mapping of the environment, detecting and analyzing static (traffic signs, power lines, vegetation, street furniture), and dynamic (traffic flow, crowd gathering, unusual events) scene elements.