ロボットはなぜ道を渡るのか？—マルチモーダルセンサデータから学ぶ自律的道路横断
Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing
レーザとレーダのデータのみを用い、ランダムフォレストで道路横断の安全可否を判定するマルチモーダル学習手法を提案し、実世界データで有効性を示した。
著者: Noha Radwan, Wera Winterhalter, Christian Dornhege, Wolfram Burgard
分類: cs.RO
原文アブストラクト
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.
