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Segmentierung von 3D-Daten
(2011)
Die vorliegende Arbeit wird im Rahmen eines Projektes des Fraunhofer Instituts IAIS erstellt. Hier geht es um die Entwicklung eines neuen 3D-Laserscanners. Basierend auf diesem 3D-Laserscanner soll eine Sicherheits-Anwendung realisiert werden. Für eine Softwarekomponente - die Segmentierung von 3D-Daten - wird der Stand der Forschung untersucht und es werden drei Segmentierungs-Verfahren ausgewählt und implementiert. Der RANSAC-Algorithmus wird zur Detektion von Ebenen eingesetzt. In dieser Arbeit wird er um ein Abbruchkriterium erweitert, welches die Gesamtlaufzeit bei der Segmentierung von mehreren Ebenen verringert.
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.
In the eld of accessing and visualization mobile sensors and their recorded data, di erent approaches were realized. The OGC1 Sensor observation Service supplies a standard to access these information, stored on servers. To be able to access these servers, an interface must be developed and implemented. The result should be a con gurable development framework for web-based GIS clients supporting the OGC sensor observation services. In particular the framework should allow continuous position updates of mobile sensors. Visualization features like charts, bounding boxes of sensors and data series should be included.
Neural network based object detectors are able to automatize many difficult, tedious tasks. However, they are usually slow and/or require powerful hardware. One main reason is called Batch Normalization (BN) [1], which is an important method for building these detectors. Recent studies present a potential replacement called Self-normalizing Neural Network (SNN) [2], which at its core is a special activation function named Scaled Exponential Linear Unit (SELU). This replacement seems to have most of BNs benefits while requiring less computational power. Nonetheless, it is uncertain that SELU and neural network based detectors are compatible with one another. An evaluation of SELU incorporated networks would help clarify that uncertainty. Such evaluation is performed through series of tests on different neural networks. After the evaluation, it is concluded that, while indeed faster, SELU is still not as good as BN for building complex object detector networks.
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 the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment. Even a well-modelled robot may fail to perform its tasks successfully due to external faults which occur because of an infinite number of unforeseeable and unmodelled situations. Through investigating the most frequent failures in typical scenarios which have been observed in real-world demonstrations and competitions using the autonomous service robots Care-O-Bot III and youBot, we identified four different fault classes caused by disturbances, imperfect perception, inadequate planning operator or chaining of action sequences. This thesis then presents two approaches to handle external faults caused by insufficient knowledge about the preconditions of the planning operator. The first approach presents reasoning on detected external faults using knowledge about naive physics. The naive physics knowledge is represented by the physical properties of objects which are formalized in a logical framework. The proposed approach applies a qualitative version of physical laws to these properties in order to reason. By interpreting the reasoning results the robot identifies the information about the situations which can cause the fault. Applying this approach to simple manipulation tasks like picking and placing objects show that naive physics holds great possibilities for reasoning on unknown external faults in robotics. The second approach includes missing knowledge about the execution of an action through learning by experimentation. Firstly, it investigates such representation of execution specific knowledge that can be learned for one particular situation and reused for situations which deviate from the original. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. This representation is called action execution model (AEM) here. The approach provides a learning strategy which uses a physical simulation for generating the training data to learn both symbolic and geometric aspects of the model. The experimental analysis, performed on two physical robots, shows that AEM can reliably describe execution specific knowledge and thereby serving as a potential model for avoiding the occurrence of external faults.
Skill generalisation and experience acquisition for predicting and avoiding execution failures
(2023)
For performing tasks in their target environments, autonomous robots usually execute and combine skills. Robot skills in general and learning-based skills in particular are usually designed so that flexible skill acquisition is possible, but without an explicit consideration of execution failures, the impact that failure analysis can have on the skill learning process, or the benefits of introspection for effective coexistence with humans. Particularly in human-centered environments, the ability to understand, explain, and appropriately react to failures can affect a robot's trustworthiness and, consequently, its overall acceptability. Thus, in this dissertation, we study the questions of how parameterised skills can be designed so that execution-level decisions are associated with semantic knowledge about the execution process, and how such knowledge can be utilised for avoiding and analysing execution failures. The first major segment of this work is dedicated to developing a representation for skill parameterisation whose objective is to improve the transparency of the skill parameterisation process and enable a semantic analysis of execution failures. We particularly develop a hybrid learning-based representation for parameterising skills, called an execution model, which combines qualitative success preconditions with a function that maps parameters to predicted execution success. The second major part of this work focuses on applications of the execution model representation to address different types of execution failures. We first present a diagnosis algorithm that, given parameters that have resulted in a failure, finds a failure hypothesis by searching for violations of the qualitative model, as well as an experience correction algorithm that uses the found hypothesis to identify parameters that are likely to correct the failure. Furthermore, we present an extension of execution models that allows multiple qualitative execution contexts to be considered so that context-specific execution failures can be avoided. Finally, to enable the avoidance of model generalisation failures, we propose an adaptive ontology-assisted strategy for execution model generalisation between object categories that aims to combine the benefits of model-based and data-driven methods; for this, information about category similarities as encoded in an ontology is integrated with outcomes of model generalisation attempts performed by a robot. The proposed methods are exemplified in terms of various use cases - object and handle grasping, object stowing, pulling, and hand-over - and evaluated in multiple experiments performed with a physical robot. The main contributions of this work include a formalisation of the skill parameterisation problem by considering execution failures as an integral part of the skill design and learning process, a demonstration of how a hybrid representation for parameterising skills can contribute towards improving the introspective properties of robot skills, as well as an extensive evaluation of the proposed methods in various experiments. We believe that this work constitutes a small first step towards more failure-aware robots that are suitable to be used in human-centered environments.
The task of this thesis is to develop an OGC-compliant Sensor Observation Service (SOS) { a component of the SWE { for GPS related sensor data in this context. It should, in contrast to existing implementations, support full mobility of the sensors and be con gurable with respect to adding di erent kinds of sensors. In particular, mobile phones should be considered as sensors, which transmit their data to the SOS server through the transactional SOS interface.
This project focuses on object detection in dense volume data. There are several types of dense volume data, namely Computed Tomography (CT) scan, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI). This work focuses on CT scans. CT scans are not limited to the medical domain; they are also used in industries. CT scans are used in airport baggage screening, assembly lines, and the object detection systems in these places should be able to detect objects fast. One of the ways to address the issue of computational complexity and make the object detection systems fast is to use low-resolution images. Low-resolution CT scanning is fast. The entire process of scanning and detection can be made faster by using low-resolution images. Even in the medical domain, to reduce the rad iation dose, the exposure time of the patient should be reduced. The exposure time of patients could be reduced by allowing low-resolution CT scans. Hence it is essential to find out which object detection model has better accuracy as well as speed at low-resolution CT scans. However, the existing approaches did not provide details about how the model would perform when the resolution of CT scans is varied. Hence in this project, the goal is to analyze the impact of varying resolution of CT scans on both the speed and accuracy of the model. Three object detection models, namely RetinaNet, YOLOv3, and YOLOv5, were trained at various resolutions. Among the three models, it was found that YOLOv5 has the best mAP and f1 score at multiple resolutions on the DeepLesion dataset. RetinaNet model h as the least inference time on the DeepLesion dataset. From the experiments, it could be asserted that sacrificing mean average precision (mAP) to improve inference time by reducing resolution is feasible.