The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Road network information (RNI) simpliﬁes autonomous driving by providing strong priors about driving environments. Its usefulness has been demonstrated in the DARPA Urban Challenge. However, the need to manually generate RNI prevents us from fully exploiting its beneﬁts. We envision an aerial image analysis system that automatically generates RNI for a route between two urban locations. As a step toward this goal, we present an algorithm that extracts the structure of a parking lot visible in an aerial image. We formulate this task as a problem of parking spot detection because extracting parking lot structures is closely related to detecting all of the parking spots. To minimize human intervention in use of aerial imagery, we devise a self-supervised learning algorithm that automatically obtains a set of canonical parking spot templates to learn the appearance of a parking lot and estimates the structure of the parking lot from the learned model. The data set extracted from a single image alone is too small to sufﬁciently learn an accurate parking spot model. To remedy this insufﬁcient positive data problem, we utilize self-supervised parking spots obtained from other aerial images as prior information and a regularization technique to avoid an overﬁtting solution.

Keywords

roadmap generation for autonomous driving, aerial image analysis, computer vision, machine learning

Notes

Sponsor: General Motors

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Enhanced Road Network Data from Overhead Imagery

Text Reference

Young-Woo Seo and Christopher Urmson, "Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures," Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2009, pp. 339-344.

BibTeX Reference

@inproceedings{Seo_2009_6464,
   author = "Young-Woo Seo and Christopher Urmson",
   title = "Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures",
   booktitle = "Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems",
   pages = "339-344",
   publisher = "IEEE",
   month = "October",
   year = "2009",
}

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