  Compared to abstract features, significant objects, so-called landmarks, are
a more natural means for vehicle localization and navigation, especially in
challenging unstructured environments. The major challenge is to recognize
landmarks in various lighting conditions and changing environment (growing
vegetation) while only having few training samples available. We propose a new
method which leverages Deep Learning as well as model-based methods to overcome
the need of a large data set. Using RGB images and light detection and ranging
(LiDAR) point clouds, our approach combines state-of-the-art classification
results of Convolutional Neural Networks (CNN), with robust model-based methods
by taking prior knowledge of previous time steps into account. Evaluations on a
challenging real-wold scenario, with trees and bushes as landmarks, show
promising results over pure learning-based state-of-the-art 3D detectors, while
being significant faster.
