Abstract-Band - Fakultät für Informatik, TU Wien - Technische ...

Institut für Automatisierungs- und Regelungstechnik
Yaroslav Barsoukov
Development of GPICP, a GPU-only registration algorithm based on Iterative
Closest Point
Studium: Masterstudium Software Engineering & Internet Computing
BetreuerIn: Ao.Univ.Prof. Dr. Markus Vincze
Der von Besl and McKay entwickelte ICP Algorithmus ist längst zum Goldstandard
fürs Lösen des Reqistrierungsproblems geworden. Nachdem sich die
Programmierbarkeit der GPUs in der letzten Zeit erhöht hat, ist ihr paralleles
Programmierungsmodel nun für ICP perfekt geeignet. Die vorliegende Arbeit
setzt sich zum Ziel, eine Implementierung von ICP zu entwerfen, die völlig auf
GPU ausgeführt werden könnte; die dabei entstehenden Probleme mit GPU
Architektur werden gelöst, und die erhöhte Leistung wird dafür benutzt, den
Algorithmus selbst präziser zu machen.
Bernhard Mathias
Object modelling with a robot arm
Studium: Masterstudium Technische Informatik
BetreuerIn: Ao.Univ.Prof. Dr. Markus Vincze
Object learning and object recognition are general research topics in the
robotics. This thesis deals with the problem of automating the learning process.
A robot should be able to explore its environment and to learn new
objects autonomously. For object modelling, an implementation of the V4R
library (of the Vision for Robotics group, ACIN, TU Vienna) is used. A model
combines image descriptors (SIFT) representing textures and point cloud
descriptors (SHOT) to describe the 3-D shape of the object. The information
stored in such models can be used to recognize objects in other scenarios
again (even amongst many other objects). In order to get 2-D RGB as well as 3-
D depth images, the Microsoft Kinect camera is used within this project. To
move the objects in front of the camera, a robotic arm of the type Neuronics
Katana 450 is used. A trajectory as optimally as possiple is necessary to receive
a complete model. The completeness of a model can be checked with the
probability sphere already implemented in V4R. The probability sphere is a
platonic body where the surface consists of several triangular faces. These
faces represent different viewpoints relative to the object. Colours represent
the completeness of the model from the according direction. White faces
indicate a high probability to detect the object from this viewpoint and black
faces indicate missing viewpoints. Grey-shaded faces represent partially
observed viewpoints. There are several subproblems within this project, for
example, the determination of the robot arm pose within the camera
coordinates (camera calibration) or the topic of how to control the arm with an
optimal trajectory. Also image processing is an important topic within this
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