Fabian Poggenhans (M.Sc.)
Wissenschaftlicher Mitarbeiter
Werdegang
Fabian Poggenhans studierte vom Oktober 2008 bis September 2014 Maschinenbau am Karlsruher Institut für Technologie. Seit Oktober 2014 ist er am FZI als Wissenschaftlicher Mitarbeiter im Bereich Mobile Preception Systems (MPS) tätig.
Schwerpunkte seiner Arbeit sind die kamberabasierte Erstellung hochgenauer Karten in urbaner Umgebung und die Erkennung, Klassifikation und Lokalisierung mit Hilfe von kartierungsrelevanten Merkmalen.
Publikationen
Zeitungs- oder Zeitschriftenartikel (3)
- Application of Line Clustering Algorithms for Improving Road Feature DetectionInfoDetails
Fabian Poggenhans, André-Marcel Hellmund and Christoph Stiller, 2016
Although many algorithms have been proposed for the camera-based detection of road features (such as road markings, curbstones and road borders), truly contextual or relational information between the detections is rarely used. This is all the more surprising, since a lot of potential remains unused, regarding outlier rejection or compensating detection failures, multiple detections, misclassification or fragmentation. The aim of this paper is to present an approach that is suitable for such a task in both online and offline applications as a post-processing step after the actual detection and classification step. This is achieved by adapting a perception-based line-clustering algorithm that groups the pre-classified road features based on their relations and assigns them a final class. The grouped features are then fused to form continuous lines instead of individual dashes or fragmented lines. The evaluation on a 10 km drive in both rural and urban environment, as well as an online test on a short highway driving sequence shows that this approach is very well capable to increase the performance of road feature detection at a low computational cost.
- A Universal Approach to Detect and Classify Road Surface MarkingsInfoDetails
Fabian Poggenhans and Markus Schreiber and Christoph Stiller, 2015
In autonomous driving, road markings are an essential element for high-precision mapping, trajectory planning and can provide important information for localization. This paper presents an approach to detect, classify and approximate a great variety of road markings using a stereoscopic camera system. We present an algorithm that is able to classify characters and arrows as well as stop-lines, pedestrian crossings, dashed and straight lines, etc. The classification is independent of orientation, position or the exact shape. This is achieved using a histogram of the marking width as main part of the feature vector for line-shaped markings and Optical Character Recognition (OCR) for characters. Classification is done by an Artificial Neural Network (ANN). We have evaluated our approach over a 10.5 km drive through an urban area.
- Detecting symbols on road surface for mapping and localization using OCR InfoDetails
Markus Schreiber and Fabian Poggenhans and Christoph Stiller, 2014
In this paper, we present a system to detect symbols on roads (e.g. arrows, speed limits, bus lanes and other pictograms) with a common monoscopic or stereoscopic camera system. No manual labeling of images is necessary since the exact definitions of the symbols in the legal instructions for road paintings are used. With those vector graphics an Optical Character Recognition (OCR) System is trained. If only a monoscopic camera is used, the vanishing point is estimated and an inverse perspective transformation is applied to obtain a distortion free top-view. In case of the stereoscopic camera setup, the 3D reconstruction is projected to a ground plane. TESSERACT, a common OCR system is used to classify the symbols. If odometry or position information is available, a spatial filtering and mapping is possible. The obtained information can be used on one side to improve localization, on the other side to provide further information for planning or generation of planning maps.
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Kontakt
Telefon: +49 721 9654-260
E-Mail: Poggenhans@fzi.de- Application of Line Clustering Algorithms for Improving Road Feature DetectionInfoDetails