Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing
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...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
18.09.2017
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1709.06039 |