vIsage - A visualization and debugging framework for distributed system applications

Co-authored

We present a Visualization, Simulation, And Graphical debugging Environment (vIsage) for distributed systems. Time-varying
spatial data as well as other information from different sources can be displayed and superimposed in a single view at run-time.
The main contribution of our framework is that it is not just a tool for visualizing the data, but it is a graphical interface for a
simulation environment. Real world data can be recorded, played back or even synthesized. This enables testing and debugging
of single components of complex distributed systems. Being the missing link between development, simulation and testing,
e.g., in robotics applications, it was designed to significantly increase the efficiency of the software development process.

Echtzeiterkennung von befahrbaren Bereichen in urbanen Szenarien

GI-Fachtagung

Unser Artikel beshreibt ein Ehtzeitverfahren zur kamerabasierten Fahrbereihserkennung, welches in urbanen bzw. ländlichen Fahrszenarien eingesetzt wird. In dem Eingabebild eines monokularen Kamerasystems, welches auf dem Dach eines Automobils in Fahrtrichtung montiert ist, wird pro Zeitschritt ein kleiner Bereich vor der Motorhaube als befahrbar vorausgesetzt. Der Algorithmus berechnet die
vorherrschenden Farben innerhalb dieses befahrbaren Bereiches und vegleicht die Farben mit den Farbwerten jedes Pixels im Eingabebild: Je ähnlicher die Pixelfarben zu den vorherrschenden sind, desto höher ist die Wahrscheinlichkeit, dass die entsprechenden Bereiche befahrbar sind. Um
den Algorithmus auch im städtischen Umfeld einsetzen zu können, muss ein vorverarbeitendes Modul vorges
haltet werden, welches Fahrspuren, Schatten und überberbeli
htete Bereiche ausmaskiert und in der entgültigen Befahrbarkeitskarte als unbekannt (rot) markiert.Weiterhin wird ein dynamisches Suchpolygon vorgestellt, um den Algorithmus unabhängig von weiteren Eingabesensoren zu gestalten.

Caroline: An autonomously driving vehicle for urban environments

Co-authored

The 2007 DARPA Urban Challenge afforded the golden opportunity for the Technische Universität Braunschweig to demonstrate its abilities to develop an autonomously driving vehicle to compete with the world's best. After several stages of qualification, our team CarOLO qualified early for the DARPA Urban Challenge Final Event and was among only 11 teams from initially 89 competitors to compete in the final. We had the ability to work together in a large group of experts, each contributing his expertise in his discipline, and significant organizational, financial, and technical support by local sponsors, who helped us to become the best non-U.S. team. In this report, we describe the 2007 DARPA Urban Challenge, our contribution, “Caroline,” the technology, and algorithms, along with her performance in the DARPA Urban Challenge Final Event on November 3, 2007.

A Fast and Robust Approach to Lane Marking Detection and Lane Tracking

Co-authored with Christian Lipski

We present a lane detection algorithm that robustly detects
and tracks various lane markings in real-time. The first
part is a feature detection algorithm that transforms several
input images into a top view perspective and analyzes local
histograms. For this part we make use of state-of-the-art
graphics hardware. The second part fits a very simple and
flexible lane model to these lane marking features. The algorithm
was thoroughly tested on an autonomous vehicle
that was one of the finalists in the 2007DARPAUrban Challenge.
In combination with other sensors, i.e. a lidar, radar
and vision based obstacle detection and surface classification,
the autonomous vehicle is able to drive in an urban
scenario at up to 15 mp/h.

The area processing unit of Caroline - Finding the way through DARPA's Urban Challenge

This paper presents a vision-based color segmentation algo-
rithm suitable for urban environments that separates an image into areas of drivable and non-drivable terrain. Assuming that a part of the image is known to be drivable terrain, other parts of the image are classied by comparing the Euclidean distance of each pixel's color to the mean colors of the drivable area in real-time. Moving the search area depend-ing on each frame's result ensures temporal consistency and coherence. Furthermore, the algorithm classies artifacts such as white and yellow lane markings and hard shadows as areas of unknown drivability. The algorithm was thoroughly tested on the autonomous vehicle 'Caroline', which was a nalist in the 2007 DARPA Urban Challenge.

A ghosting artifact detector for interpolated image quality assessment

to appear in Proceedings of ACM Applied Perception in Computer Graphics and Visualization 

We present a no-reference image quality metric for image interpolation.
The approach is capable of detecting ghosting artifacts, e.g.,
in image based rendering scenarios. Based on the assumption that
ghosting artifacts can be detected locally, the perceived visual quality
can be predicted from the amount of regions that are affected by
ghosting. Because the approach does not require any reference image,
it is very suitable, e.g., for assessing the quality of image-based
rendering techniques in general settings.

A ghosting artifact detector for interpolated image quality assessment

Technical Report/ Computer Graphics Lab, TU Braunschweig; 2009-7-10 

We present a no-reference image quality metric for image interpolation. The
approach is capable of detecting ghosting artifacts, e.g., in image based
rendering scenarios. Based on the assumption that ghosting artifacts can be
detected locally, perceived visual quality can be predicted from the amount
of regions that are affected by ghosting. Because the approach does not
require any reference image, it is very suitable, e.g., for assessing quality of
image-based rendering techniques in general settings.

Tomographic 4D Reconstruction of Gas Flows in the Presence of Occluders