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Comparison of Time Warping Algorithms for Rail Vehicle Velocity Estimation in Low Speed Scenarios
(2017)
Die in dieser Arbeit vorgestellte Vorgehensweise erlaubt die Ortung von Schienenfahrzeugen in topologischen Karten allein mit Hilfe eines Wirbelstromsensorsystems (WSS). Zur Ortung primär erforderlich ist die Identifizierung des befahrenen Gleises selbst, wofür unterschiedliche in einer Karte gespeicherte Merkmale herangezogen werden sowie der zurückgelegte Weg, der durch Zählen der passierten Schwellen ermittelt wird. Diese Merkmale werden mittels eigens definierter, virtueller Sensoren aus dem Signal des WSS gewonnen und mittels einem Bayes’schen Formalismus mit den Referenzdaten aus der vorliegenden topologischen Karte abgeglichen. Diese auf virtuellen Sensoren basierende Vorgehensweise erlaubt eine Parallelisierung der Sensorsignalverarbeitung und eine flexible Einbindung von Sensoren in das Ortungssystem. Die Möglichkeit, Weichen mit einer Trefferquote von 99% zu detektieren, erlaubt die Verfolgung der Fahrzeugposition über die gesamte Fahrstrecke hinweg, unter alleiniger Verwendung der vom WSS gelieferten Messdaten.
The need to measure basic aerosol parameters has increased dramatically in the last decade. This is due mainly to their harmful effect on the environment and on public health. Legislation requires that particle emissions and ambient levels, workplace particle concentrations and exposure to them are measured to confirm that the defined limits are met and the public is not exposed to harmful concentrations of aerosols.
Für die genaue Positionsbestimmung in Innenräumen, beispielsweise in Bahnhöfen oder Einkaufszentren, soll in dem beschriebenen Projekt untersucht werden, inwiefern lokale Magnetfelder genutzt werden können, um Genauigkeit und Robustheit zu erhöhen. Hierzu wird untersucht, ob und wie kostengünstige Magnetfeldsensoren und mobile Roboterplattformen genutzt werden können, um Karten zu erstellen, die eine spätere Navigation, beispielsweise mit Smartphones oder mit anderen mobilen Geräten.
The precise positioning of mobile systems is a prerequisite for any autonomous behavior, in an industrial environment as well as for field robotics. The paper describes the set up for an experimental platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. Two approaches are compared. First, a local method based on point cloud matching and integration of inertial measurement units is evaluated. Subsequent matching makes it possible to create a three-dimensional point cloud that can be used as a map in subsequent runs. The second approach is a full SLAM algorithm, based on graph relaxation models, incorporating the full sensor suite of odometry, inertial sensors, and 3D laser scan data.
A novel approach for synchronization and calibration of a camera and an inertial measurement unit (IMU) in the research-oriented visual-inertial mapping-and localization-framework maplab is presented. Mapping and localization are based on detecting different features in the environment. In addition to the possibility of creating single-case maps, the included algorithms allow merging maps to increase mapping accuracy and obtain large-scale maps. Furthermore, the algorithms can be used to optimize the collected data. The preliminary results show that after appropriate calibration and synchronization maplab can be used efficiently for mapping, especially in rooms and small building environments.
This paper deals with the detection and segmentation of clouds on high-dynamic-range (HDR) images of the sky as well as the calculation of the position of the sun at any time of the year. In order to predict the movement of clouds and the radiation of the sun for a short period of time, the clouds thickness and position have to be known as precisely as possible. Consequently, the segmentation algorithm has to provide satisfactory results regardless of different weather, illumination and climatic conditions. The principle of the segmentation is based on the classification of each pixel as a cloud or as a sky. This classification is usually based on threshold methods, since these are relatively fast to implement and show a low computational burden. In order to predict if and when the sun will be covered by clouds, the position of the sun on the images has to be determined. For this purpose, the zenith and azimuth angles of the sun are determined and converted into XY coordinates.
Solar irradiance prediction is vital for the power management and the cost reduction when integrating solar energy. The study is towards a ground image based solar irradiance prediction which is highly dependent on the cloud coverage. The sky images are collected by using ground based sky imager (fisheye lens). In this work, different algorithms for cloud detection being a preparation step for their segmentation are compared.
The fisheye camera has been widely studied in the field of ground based sky imagery and robot vision since it can capture a wide view of the scene at one time. However, serious image distortion is a major drawback hindering its wider use. To remedy this, this paperproposes a lens calibration and distortion correction method for detecting clouds and forecasting solar radiation. Finally, the radial distortion of the fisheye image can be corrected by incorporating the estimated calibration parameters. Experimental results validate the effectiveness of the proposed method.