Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing
This addresses a practical challenge for autonomous robots performing tasks like delivery and surveillance in urban environments, though it appears incremental as it applies existing methods (Random Forests) to a specific domain.
The paper tackles the problem of enabling robots to autonomously cross streets without pedestrian traffic lights by developing a multi-modal learning approach using laser and radar data, which achieves safe and accurate crossing behavior that generalizes well across different situations.
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.