Stefan Reitmann

Chemnitz University of Technology · Department of Computer Science

While research will enable the deployment of autonomous systems in harsh and inaccessible environments, their operation may be interrupted due to unforeseen situations. A possibility to recover operation nonetheless is to employ teleoperation. However, what requirements and criteria need to be fulfilled by such a system when deployed in safety-crit...

While research will enable deployment of autonomous system in harsh and inaccessible environments, their operation may be interrupted due to unforeseen situations. A possibility to recover operation nonetheless is to employ teleoperation. However, what requirements and criteria need to be fulfilled by such a system when deployed in safety-critical...

Climate change poses special and new challenges to inland waters, requiring intensive monitoring. An application based on an autonomous operation swimming vehicle (ASV) is being developed that will provide simulations, spatially and depth-resolved water parameter monitoring, bathymetry detection, and respiration measurement. A clustered load system...

This paper shows a way to model moving swarms of fish in water bodies and detect them using virtual sensors. Real waters (e.g. lakes) can be explored and analyzed with sonar sensors based on sound waves. For automatic Artificial Intelligence (AI)-based detection of moving objects, such as fish, in sensor data, there is often a lack of training data...

This paper discusses and explores applications of virtual worlds generated from heterogenous kinds of open geodata, such as 2D maps, digital elevation models and aerial photographs. Two workflows are presented for generation of such virtual worlds and their integration into the Unreal and Godot game engines. The feature-richness of game engines the...

Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent iden...

Efficient airport operations depend on appropriate actions and reactions to current constraints. Local weather events and their impact on airport performance may have network-wide effects. The classification of expected weather impacts enables efficient consideration in airport operations on a tactical level. We classify airport performance with re...

We derive absolute observation stability and instability results for controlled evolutionary inequalities which are based on frequency-domain characteristics of the linear part of the inequalities. The uncertainty parts of the inequalities (nonlinearities which represent external forces and constitutive laws) are described by certain local and inte...

This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully...

This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully...

As part of the RoBiMo project, a measuring system for holistic, spatially resolved recording of the condition and processes in inland waters is to be developed. This system should measure regular, continuous and spatially re-solved parameters of water quality in stagnant inland waters. Forthat, the swimming robot "Elisabeth" from the TU Bergakademi...

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the trai...

With this collaborative work of the Freiberg University of Mining and Technology and the St. Petersburg State University capabilities and characteristics of Spiking Neural Networks in the field of computer vision and environmental robotics are to be explored and analyzed. We aim at providing an adaptive recognition and classification model for temp...

Der Luftverkehr stellt ein komplexes Gesamtsystem dar, in welchem eine Prozessoptimierung aufgrund zahlreicher und verschiedenartiger Arbeitsabläufe verschiedener Unternehmen nur durch eine übergeordnete Leistungsbewertung möglich ist. Hierfür wurde im Bereich des leistungsbasierten Flughafenmanagements - sowohl auf wissenschaftlicher, als auch ind...

Today, supervised learning is a common method for classification and segmentation tasks. The data foundation is often insufficient for suitable training of such an artificial intelligence, especially if predefined sensor technology has to be considered. For this reason, we have developed an add-on package for the open source software Blender, which...

Die Projekte RoBiMo (Robotergestütztes Binnengewässer-Monitoring) und AIRGEMM (Artificial Intelligence and Robotics for GeoEnvironmental Modelling and Monitoring) an der Technischen Universität Bergakademie Freiberg wollen zu einer deutlichen Verbesserung des Gewässermanagements beitragen. Bisherige Einschränkungen der Frequenz und Intensität der Ü...

Classification of weather impacts on airport operations will allow efficient consideration of local weather events in network-wide analysis. We use machine learning approaches in our contribution to correlate weather data from meteorological reports and airport performance data, which contains flight plan data with scheduled, actual movements and d...

Classification of weather impacts on airport operations will allow efficient consideration of local weather events in network-wide analysis. We use machine learning approaches to correlate weather data from meteorological reports and airport performance data contains of flight plan data with scheduled and actual movements as well as delays. In part...

Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft t...

In this paper we compute the impact of weather events to airport performance, which is measured as deviation of actual and scheduled timestamps (delay). Weather phenomena are categorized by the Air Traffic Management Airport Performance (ATMAP) weather algorithm, which aims to quantify weather conditions at European airports. A comprehensive datase...

In this paper we compute the impact of weather events to airport performance, which is measured as deviation of actual and scheduled timestamps (delay). Weather phenomena are categorized by the Air Traffic Management Airport Performance weather algorithm, which aims to quantify weather conditions at European airports. A comprehensive dataset of fli...

In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding i...

In this paper we compute the impact of weather events to airport performance, which is measured as deviation of actual and scheduled timestamps (delay). Weather phenomena are categorized by the Air Traffic Management Airport Performance weather algorithm, which aims to quantify weather conditions at European airports. A comprehensive dataset of fli...

Reliable and predictable ground operations are essential for 4D aircraft trajectories. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaro...

The performance analysis of complex systems like Air Traffic Management (ATM) is a challenging task. To overcome statistical complexities through analyzing non-linear time series we approach the problem with machine learning methods. Therefore we understand ATM (and its identified system model) as a system of coupled and interdependent sub-systems...

Understanding and quantifying aviation as a whole system of coupled and interdependent sub-systems is a challenging task. To overcome resulting complexities and dynamic effects we approach the problem from an analytical and operational point of view. Both differ in their required data inputs as well as their methods to analyze and identify interdep...