@inproceedings {pub3475,
	title = {Strategies for Improving Camera to Map Alignment},
	author = {Johannes Silberbauer AND Benedict Flade AND Stephan Hasler AND Malte Probst  AND Julian Eggert},
	year = {2017},
	month = {September},
	abstract = {Accurate localisation of the ego vehicle relative to the map is a key requirement for most advanced driver assistance systems (ADAS). Building on a previous approach that aligns monocular camera images to perspectivly rendered Open Street Map (OSM) data we introduce a way to apply machine learning (ML) in that context. To that end we compare two strategies for improving the previous approach: First, we enhance the original feature extraction step by using a deep model for predicting visible road area in the camera image.  Secondly, we use a model that is trained end-to-end to directly predict the correct alginemennt. We evaluate the approach on a dataset of KITTI recordings and present current challenges like obtaining accurate ground truth data.},
	publisher = {Machine Learning Reports},
	booktitle = {Workshop New Challenges in Neural Computation (NC2)}
}