A Fast Line Finder for Vision-Guided Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Parking Lot Analysis and Visualization from Aerial Images
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Journal of Cognitive Neuroscience
An Algorithm for Parking Lot Occupation Detection
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments
International Journal of Robotics Research
Hi-index | 0.00 |
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.