  We introduce the DROW detector, a deep learning based detector for 2D range
data. Laser scanners are lighting invariant, provide accurate range data, and
typically cover a large field of view, making them interesting sensors for
robotics applications. So far, research on detection in laser range data has
been dominated by hand-crafted features and boosted classifiers, potentially
losing performance due to suboptimal design choices. We propose a Convolutional
Neural Network (CNN) based detector for this task. We show how to effectively
apply CNNs for detection in 2D range data, and propose a depth preprocessing
step and voting scheme that significantly improve CNN performance. We
demonstrate our approach on wheelchairs and walkers, obtaining state of the art
detection results. Apart from the training data, none of our design choices
limits the detector to these two classes, though. We provide a ROS node for our
detector and release our dataset containing 464k laser scans, out of which 24k
were annotated.
