Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing

Noha Radwan, Wera Winterhalter, Christian Dornhege, Wolfram Burgard

Abstract:

We consider the problem of developing robots that
navigate like pedestrians on sidewalks through city centers for
performing various tasks including delivery and surveillance.
One particular challenge for such robots is crossing streets
without pedestrian traffic lights. To solve this task the robot
has to decide based on its sensory input if the road is clear. In
this work, we propose a novel multi-modal learning approach
for the problem of autonomous street crossing. Our approach
solely relies on laser and radar data and learns a classifier based
on Random Forests to predict when it is safe to cross the road.
We present extensive experimental evaluations using real-world
data collected from multiple street crossing situations which
demonstrate that our approach yields a safe and accurate street
crossing behavior and generalizes well over different types of
situations. A comparison to alternative methods demonstrates
the advantages of our approach.