L Noha Radwan - Autonomous Intelligent Systems
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Freiburg Street Crossing Dataset

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.

Terms of use

This program is provided for research purposes only. Any commercial use requires our consent. If you use the dataset you should cite our paper:

  @INPROCEEDINGS{radwan17iros,
  author    = {Noha Radwan and Wera Winterhalter and Christian Dornhege and Wolfram Burgard},
  title     = {Why Did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing},
  booktitle   = {Proc.~of the IEEE Int.~Conf.~on Intelligent Robots and Systems (IROS)},
  year      = {2017},
  URL         = {http://ais.informatik.uni-freiburg.de/publications/papers/radwan17iros.pdf}
} 

  

Links

Dataset 6,3GB
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