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We introduce the DROW detector, a deep learning based detector for 2D rangedata. Laser scanners are lighting invariant, provide accurate range data, andtypically cover a large field of view, making them interesting sensors forrobotics applications. So far, research on detection in laser range data hasbeen dominated by hand-crafted features and boosted classifiers, potentiallylosing performance due to suboptimal design choices. We propose a ConvolutionalNeural Network (CNN) based detector for this task. We show how to effectivelyapply CNNs for detection in 2D range data, and propose a depth preprocessingstep and voting scheme that significantly improve CNN performance. Wedemonstrate our approach on wheelchairs and walkers, obtaining state of the artdetection results. Apart from the training data, none of our design choiceslimits the detector to these two classes, though. We provide a ROS node for ourdetector and release our dataset containing 464k laser scans, out of which 24kwere annotated.

DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data