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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.
The applicability of characteristics of local magnetic fields for more precise determination of localization of subjects and/or objects in indoor environments, such as railway stations, airports, exhibition halls, showrooms, or shopping centers, is considered. An investigation has been carried out to find out whether and how low-cost magnetic field sensors and mobile robot platforms can be used to create maps that improve the accuracy and robustness of later navigation with smartphones or other devices.
Bei dem vorgestellten Ansatz soll der Auftreffpunkt des Pfeils durch die Kreuzkorrelation von Audio-Signalen bestimmt werden. Das Auftreffen des Pfeils erzeugt ein charakteristisches Geräusch, welches von mehreren Mikrofonen in bestimmter Anordnung um die Dartscheibe herum in elektrische Signale umgewandelt wird. Mithilfe der Schallgeschwindigkeit und den Zeitdifferenzen, welche die Schallwelle zu den einzelnen Mikrofonen benötigt soll dann der Auftreffpunkt berechnet werden.
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.
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 visual-inertial mapping and localization system maplab is analyzed by its implementation and subsequent evaluation. The mapping or localization is based on environmental feature detection. In addition to creating maps, there is also the option of fusion of several maps and thus mapping extensive areas and using them for further analysis of data. In this way, various software tools can be used to optimize the existing data sets.
Two sensor components are needed: an inertial measuring unit (IMU) and a monochrome camera, which are combined by a hardware rig and put into operation for the analysis of the visual-inertial system. System calibration is crucial for precision and system functioning and is based on nonlinear dynamic state estimation. This ensures the best possible estimate of the position of the environmental feature and the map. Maplab is particularly suitable for mapping rooms or small building complexes as the implementation and evaluation of the results in different application scenarios show. Special emphasis is laid on the evaluation of larger scenarios, in which is shown, that the system is struggling to keep up geometric consistencies and thus provide an accurate map.
Evaluierung von Kalman Filter Konfigurationen zur Roboterlokaliserung mittels Sensordatenfusion
(2023)
In dieser Arbeit werden drei verschiedene Konfigurationen der von Tom Moore, für das Robot Operating System, entwickelte Kalman-Filter vorgestellt. Diese bilden die Grundlage für eine Lokalisierung mittels Sensorfusion in dem verwendeten ROS-Framework. Ziel dieser Arbeit ist der Aufbau und die Verifikation einer Lokalisierung für ein mobiles Robotersystem Husky A200 der Firma Clearpath Robotics. Hierzu wurden die Möglichkeiten des bestehenden Systems untersucht und mehrere Versionen von Lokalisierungsfiltern konfiguriert. Am an Ende, wird eine Verifikation der Ergebnisse in verschiedenen Szenarien gegeneinandergestellt. Hierzu werden die Ergebnisse einer Variante des Extended Kalman-Filters in 2D (EKF2D), eine Variante des Unscented Kalman-Filter in 2D (UKF2D) und eine Variante des Extended Kalman-Filters in 3D (EKF3D) verifiziert und verglichen. Die Untersuchungen ergaben das der EKF2D die besten und robustesten Ergebnisse für eine Lokalisierung erbringt, trotz, im Vergleich zu der UKF2D Variante, 17,3 % höhere Endpositionsabweichung aufweist. Die in diesem Projekt gewählte EKF3D Konfigurationsvariante eignet sich, wegen seinen starken Ungenauigkeiten in der Höhenbestimmung nicht für eine aussagekräftige Positionsbestimmung.
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.
The aim of this work is the application and evaluation of a method to visually detect markers at a distance of up to five meters and determine their real-world position. Combinations of cameras and lenses with different parameters were studied to determine the optimal configuration. Based on this configuration, camera images were taken after proper calibration. These images are then transformed into a bird's eye view using a homography matrix. The homography matrix is calculated with four-point pairs as well as with coordinate transformations. The obtained images show the ground plane un distorted, making it possible to convert a pixel position into a real-world position with a conversion factor. The proposed approach helps to effectively create data sets for training neural networks for navigation purposes.
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.