Self-supervised aerial image analysis for extracting parking lot structure

Young-Woo Seo, Nathan Ratliff, and Christopher Urmson
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-09), July, 2009.


Abstract
Road network information simplifies autonomous driving by providing strong priors about environments. It informs a robotic vehicle with where it can drive, models of what can be expected, and contextual cues that influence driving behaviors. Currently, however, road network information is manually generated using a combination of GPS survey and aerial imagery. These manual techniques are labor intensive and error prone. To fully exploit the benefits of digital imagery, these processes should be automated. As a step toward this goal, we present an algorithm that extracts the structure of a parking lot visible from a given aerial image. To minimize human intervention in the use of aerial imagery, we devise a self-supervised learning algorithm that automatically generates a set of 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. However, strong priors trained using large data sets collected across multiple images dramatically improve performance. Our self-supervised approach outperforms the prior alone by adapting the distribution of examples toward that found in the current image. A thorough empirical analysis compares leading state-of-the-art learning techniques on this problem.

Keywords
navigation map generation for autonomous vehicle, machine learning, computer vision

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, Nathan Ratliff, and Christopher Urmson, "Self-supervised aerial image analysis for extracting parking lot structure," Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-09), July, 2009.

BibTeX Reference
@inproceedings{Seo_2009_6371,
   author = "Young-Woo Seo and Nathan Ratliff and Christopher Urmson",
   title = "Self-supervised aerial image analysis for extracting parking lot structure",
   booktitle = "Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-09)",
   publisher = "AAAI Press",
   month = "July",
   year = "2009",
}