Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

Filters

4 1
Reset filters

Settings
Now showing 1 - 5 of 5
No Thumbnail Available
Publication

From multi-sensor aerial data to thermal and infrared simulation of semantic 3D models: Towards identification of urban heat islands

2020 , Bulatov, Dimitri , Burkard, Eva , Ilehag, Rebecca , Kottler, Benedikt , Helmholz, Petra

Urban heat islands degrade the quality of life in many urban centers. To achieve their detection in urban canopy and to predict their development in the future, infrared simulation turns out to be a suitable tool. For simulation of the temperature, various scene properties must be taken into account. Starting at raw sensor data acquired from the air, we developed an end-to-end pipeline to the semantic mesh, in which temperatures and radiance can be simulated depending on actual weather data and initial conditions and which has a potential to track the urban heat islands. To acquire the mesh, we focus on retrieving land cover classes and 3D geometry. The land cover map helps to identify buildings, to update the existing geographic maps, and to analyze building roofs with respect to their materials and thus, sustainability. The 3D geometry basically presupposes storing the scene efficiently into triangles. For each triangle, we are not only interested in material properties, but also in neighborhood relations allowing to model heat conduction. Together with terms for convection and radiation, we formulate the heat balance equation and compute the surface temperature as a function of time. The pipeline was tested on a dataset from a large Australian city exhibiting most properties which bear risks to contribute to heat islands: Its location in a subtropical (Mediterranean) climate zone, rapidly growing population, and, at least initially, a certain lack of sensibility towards sustainable management of resources and materials. To analyze both latter factors, two intermediate results from our method, namely tracking urbanization degree and identification of common roofing materials, are addressed and thoroughly evaluated in the dataset. It could be deduced that the area occupied by buildings increased by roughly 5% and that roughly every 6th building has a steel roof. Finally, high similarities with the ground truth were achieved both for temperature curves in some 20 test points and for large-scale evaluation. Deviations from the ground truth emerge in case of building roofs leading to the conclusion that the inner model assumption could be less accurate and, therefore, runs the danger to increase the urban heat island effect.

View
No Thumbnail Available
Publication

Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

2019 , Hillemann, Markus , Weinmann, Martin , Mueller, Markus S. , Jutzi, Boris

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

View
No Thumbnail Available
Publication

Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry

2020 , Zieger, Toni , Nagel, S. , Lutzmann, Peter , Kaufmann, Ilja , Ritter, J. , Ummenhofer, T. , Knödel, P. , Fischer, Peter

This work compares continuous seismic ground motion recordings over several months on top of the foundation and in the near field of a wind turbine (WT) at Pfinztal, Germany, with numerical tower vibration simulations and simultaneous optical measurements. We are able to distinguish between the excitation of eigenfrequencies of the tower-nacelle system and the influence of the blade rotation on seismic data by analyzing different wind and turbine conditions. We can allocate most of the major spectral peaks to either different bending modes of the tower, flapwise, and edgewise bending modes of the blades or multiples of the blade-passing frequency after comparing seismic recordings with tower simulation models. These simulations of dynamic properties of the tower are based on linear modal analysis performed with finite beam elements. To validate our interpretations of the comparison of seismic recordings and simulations, we use optical measurements of a laser Doppler vibrometer at the tower of the turbine at a height of about 20 m. The calculated power spectrum of the tower vibrations confirms our interpretation of the seismic peaks regarding the tower bending modes. This work gives a new understanding of the source mechanisms of WT-induced ground motions and their influence on seismic data by using an interdisciplinary approach. Thus, our results may be used for structural health purposes as well as the development of structural damping methods, which can also reduce ground motion emissions from WTs. Furthermore, it demonstrates how numerical simulations of wind turbines can be validated by using seismic recordings and laser Doppler vibrometry.

View
No Thumbnail Available
Publication

55 W actively Q-switched single oscillator Tm3+, Ho3+-codoped silica polarization maintaining 2.09 µm fiber laser

2019 , Dalloz, Nicolas , Robin, Thierry , Cadier, Benoît , Kieleck, Christelle , Eichhorn, Marc , Hildenbrand-Dhollande, Anne

A bidirectional 793 nm diode-pumped actively Q-switched Tm3+, Ho3+-codoped silica polarization-maintaining (PM) double-clad (DC) fiber laser is reported. With this fiber laser, 55 W of average output power with 100 ns pulse width at 200 kHz repetition rate and 2.09 µm wavelength is obtained. The pump power injection with end-caps fusion-spliced on fiber tips provides good power stability (< 1.1%) and beam quality factors (M2 < 1.7). The fiber laser output beam polarization factor is 97.5%. At 55 W, no thermal-induced damage is observed on any optical element, and power scaling of the laser is only pump-power-limited in the range of the total available pump power (180 W).

View
No Thumbnail Available
Publication

Practical approach for synthetic aperture radar change analysis in urban environments

2019 , Boldt, Markus , Thiele, Antje , Schulz, Karsten , Meyer, Franz J. , Hinz, Stefan

Change detection using remote sensing imagery is a broad and highly active field of research that has produced many different technical approaches for multiple applications. The majority of these approaches have in common that they do not deliver any detailed information concerning the type, category, or class of the detected changes. With respect to the extraction of such information, recent research often suggests that a land use classification is required. This classification can be accomplished in an unsupervised or supervised way, whereas the practicability of both strategies is more or less limited by the usage of reference or training data. Moreover, expert knowledge is needed to arrive at meaningful land use classes. An approach is presented that overcomes these drawbacks. A time series of synthetic aperture radar amplitude images is considered, enabling the detection of so-called high activity objects in urban environments. Such objects represent the basis of the investigations and denote the input for unsupervised categorization and classification procedures. The method supports even the unexperienced user in learning the actual information content leading to the capability to define a suitable scheme for change classification. Tests carried out on two different datasets suggest that the method is both practical and robust.

View