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This report presents an approach on a quadrotor dynamics stabilization based on ICP SLAM. Because the quadrotor lacks sensory information to detect its horizontal drift an additional sensor as Hokuyo-UTM has been used to perform on-line ICP-based SLAM. The obtained position estimates were used in control loops to maintain desired position and orientation of the vehicle. Such attitude parameters as height, yaw and position in space were controlled based on the laser data. As a result the quadrotor demonstrated two significant for autonomous navigation capabilities: performance of on-line SLAMon a flying vehicle and maintaining desired position in 3D space. Visual approach on optical flow based on Pyramid Lucas-Kanade algorithm has been touched and tested in different environmental conditions though hasn't been implemented in the control loop. Also the performance of the Hokuyo laser scanner and the related to it ICP SLAM algorithm have been tested in different environmental conditions indoors, outdoors and in presence of smoke. Results are presented and discussed. The requirement of performing on-line SLAM algorithm and to carry quite heavy equipment for it forced to seek a solution to increase the payload of the quadrotor with its computational power. A new hardware and distributed software architectures are therefore presented in the report.
In order to help journalists investigate inside large audiovisual archives, as maintained by news broadcast agencies, the multimedia data must be indexed by text-based search engies. By automatically creating a transcript through automatic speech recognition (ASR), the spoken word becomes accessible to text search, and queries for keywords are made possible. But stil, important contextual information like the identity of the speaker is not captured. Especially when gathering original footage in the political domain, the identity of the speaker can be the most important query constraint, although this name may not be prominent in the words spoken. It is thus desireable to have this information provided explicitely to the search engine. To provide this information, the archive must be an alyzed by automatic Speaker Identification (SID). While this research topic has seen substantial gains in accuracy and robustness over last years, it has not yet established itself as a helpful, large-scale tool outside the research community. This thesis sets out to establish a workflow to provide automatic speaker identification. Its application is to help journalists searching on speeches given in the German parliament (Bundestag). This is a contribution to the News-Stream 3.0 project, a BMBF funded research project that addresses accessibility of various data sources for journalists.
This work extends the affordance-inspired robot control architecture introduced in the MACS project [35] and especially its approach to integrate symbolic planning systems given in [24] by providing methods to automated abstraction of affordances to high-level operators. It discusses how symbolic planning instances can be generated automatically based on these operators and introduces an instantiation method to execute the resulting plans. Preconditions and effects of agent behaviour are learned and represented in Gärdenfors conceptual spaces framework. Its notion of similarity is used to group behaviours to abstract operators based on the affordance-inspired, function-centred view on the environment. Ways on how the capabilities of conceptual spaces to map subsymbolic to symbolic representations to generate PDDL planning domains including affordance-based operators are discussed. During plan execution, affordance-based operators are instantiated by agent behaviour based on the situation directly before its execution. The current situation is compared to past ones and the behaviour that has been most successful in the past is applied. Execution failures can be repaired by action substitution. The concept of using contexts to dynamically change dimension salience as introduced by Gärdenfors is realized by using techniques from the field of feature selection. The approach is evaluated using a 3D simulation environment and implementations of several object manipulation behaviours.
In der vorliegenden Arbeit wird ein Verfahren zur Segmentierung von Außenszenen und Terrain-Klassifkation entwickelt. Dazu werden 360 Grad-Laserscanner-Aufnahmen von Straßen, Gebäudefassaden und Waldwegen aufgenommen. Von diesen Aufnahmen werden verschiedene visuelle Repräsentationen in 2D erstellt. Dazu werden die Distanzinformationen und Winkelübergänge der Polarkoordinaten, die Remissionswerte und der Normalenvektor eingesetzt. Die Berechnung des Normalenvektors wird über ein modernes Verfahren mit einerniedrigen Laufzeit durchgeführt. Anschließend werden Oberflächeneigenschaften innerhalb einer Punktwolke analysiert und vier Klassen unterschieden: Untergrund, Vegetation, Hindernis und Himmel. Die Segmentierung und Klassifkation geschieht in einem Schritt. Dazuwird die Varianz auf den N ormalen über eine Filtermaske berechnet und ein Deskriptor erstellt. Der Deskriptor beinhaltet die Normalenvektoren und die Normalenvarianz fürdie x-, y- und z-Achse. Die Ergebnisse werden als Überblendung auf dem Remissionsbilddargestellt. Die Auswertung wird über eigens erstellte Ground-Truth-Daten vorgenommen. Dazu wird das Remissionsbild genutzt und der Ground-Truth mit verschiedenen Farben eingezeichnet. Die Klassifkationsergebnisse sind in Precision-Recall-Diagrammen dargestellt.
Scientists and engineers are using a distributed system Remote Component Environment (RCE) to design and simulate complex systems like airplanes, ships and satellites. During the simulation, RCE executes local and remote code. Remote code is classified as untrusted code. The execution of remote code comprises potential security risks for the host system of RCE. Additionally, RCE provides full access to system resources. The objective of this thesis is to implement a sandbox prototype to reduce the vulnerability of RCE during the execution of remote code.
Das Optimalziel für ein Logistiklager ist eine hohe Auslastung des Transportsystems. Es stellt sich somit die Frage nach der Auswahl der Aufträge, die gleichzeitig innerhalb des Lagers abgearbeitet werden, ohne Staus, Blockaden oder Überlastungen entstehen zu lassen. Dieser Auswahlprozess wird auch als Path-Packing bezeichnet. Diese Masterthesis untersucht das Path-Packing auf graphentheoretischer Ebene und stellt verschiedene Greedy-Heuristiken, eine Optimallösung auf Basis der Linearen Programmierung sowie einen kombinierten Ansatz gegenüber. Die Ansätze werden anhand von Messzeiten und Auslastungen unterschiedlich randomisiert erstellter Testdaten ausgewertet.
Semantic Image Segmentation Combining Visible and Near-Infrared Channels with Depth Information
(2015)
Image understanding is a vital task in computer vision that has many applications in areas such as robotics, surveillance and the automobile industry. An important precondition for image understanding is semantic image segmentation, i.e. the correct labeling of every image pixel with its corresponding object name or class. This thesis proposes a machine learning approach for semantic image segmentation that uses images from a multi-modal camera rig. It demonstrates that semantic segmentation can be improved by combining different image types as inputs to a convolutional neural network (CNN), when compared to a single-image approach. In this work a multi-channel near-infrared (NIR) image, an RGB image and a depth map are used. The detection of people is further improved by using a skin image that indicates the presence of human skin in the scene and is computed based on NIR information. It is also shown that segmentation accuracy can be enhanced by using a class voting method based on a superpixel pre-segmentation. Models are trained for 10-class, 3-class and binary classification tasks using an original dataset. Compared to the NIR-only approach, average class accuracy is increased by 7% for 10-class, and by 22% for 3-class classification, reaching a total of 48% and 70% accuracy, respectively. The binary classification task, which focuses on the detection of people, achieves a classification accuracy of 95% and true positive rate of 66%. The report at hand describes the proposed approach and the encountered challenges and shows that a CNN can successfully learn and combine features from multi-modal image sets and use them to predict scene labeling.
The objective of this thesis is to implement a computer game based motivation system for maximal strength testing on the Biodex System 3 Isokinetic Dynamometer. The prototype game has been designed to improve the peak torque produced in an isometric knee extensor strength test. An extensive analysis is performed on a torque data set from a previous study. The torque responses for five second long maximal voluntary contractions of the knee extensor are analyzed to understand torque response characteristics of different subjects. The parameters identifed in the data analysis are used in the implementation of the 'Shark and School of Fish' game. The behavior of the game for different torque responses is analyzed on a different torque data set from the previous study. The evaluation shows that the game rewards and motivates continuously over a repetition to reach the peak torque value. The evaluation also shows that the game rewards the user more if he overcomes a baseline torque value within the first second and then gradually increase the torque to reach peak torque.
This master thesis describes a supervised approach to the detection and the identification of humans in TV-style video sequences. In still images and video sequences, humans appear in different poses and views, fully visible and partly occluded, with varying distances to the camera, at different places, under different illumination conditions, etc. This diversity in appearance makes the task of human detection and identification to a particularly challenging problem. A possible solution of this problem is interesting for a wide range of applications such as video surveillance and content-based image and video processing. In order to detect humans in views ranging from full to close-up view and in the presence of clutter and occlusion, they are modeled by an assembly of several upper body parts. For each body part, a detector is trained based on a Support Vector Machine and on densely sampled, SIFT-like feature points in a detection window. For a more robust human detection, localized body parts are assembled using a learned model for geometric relations based on Gaussians. For a flexible human identification, the outward appearance of humans is captured and learned using the Bag-of-Features approach and non-linear Support Vector Machines. Probabilistic votes for each body part are combined to improve classification results. The combined votes yield an identification accuracy of about 80% in our experiments on episodes of the TV series "Buffy the Vampire Slayer". The Bag-of-Features approach has been used in previous work mainly for object classification tasks. Our results show that this approach can also be applied to the identification of humans in video sequences. Despite the difficulty of the given problem, the overall results are good and encourage future work in this direction.
Augmented Reality (AR) findet heutzutage sehr viele Anwendungsbereiche. Durch die Überlagerung von virtuellen Informationen mit der realen Umgebung eignet sich diese Technologie besonders für die Unterstützung der Benutzer bei technischen Wartungs- oder Reparaturvorgängen. Damit die virtuellen Daten korrekt mit der realen Welt überlagert werden, müssen Position und Orientierung der Kamera durch ein Trackingverfahren ermittelt werden. In dieser Arbeit wurde für diesen Zweck ein markerloses, modellbasiertes Trackingsystem implementiert. Während einer Initialisierungs-Phase wird die Kamerapose mithilfe von kalibrierten Referenzbildern, sogenannten Keyframes, bestimmt. In einer darauffolgenden Tracking-Phase wird das zu trackende Objekt weiterverfolgt. Evaluiert wurde das System an dem 1:1 Trainingsmodell des biologischen Forschungslabors Biolab, welches von der Europäischen Weltraumorganisation ESA zur Verfügung gestellt wurde.