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Carnegie Mellon Robotics Institute

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Abstract

This paper addresses the problem of reliably detecting parking spots in semi-filled parking lots using onboard laser line scanners. In order to identify parking spots, one needs to detect parked vehicles and interpret the parking environment. Our approach uses a supervised learning technique to achieve vehicle detection by identifying vehicle bumpers from laser range scans. In particular, we use AdaBoost to train a classifier based on relevant geometric features of data segments that correspond to car bumpers. Using the detected bumpers as landmarks of vehicle hypotheses, our algorithm constructs a topological graph representing the structure of the parking space. Spatial analysis is then performed on the topological graph to identify potential parking spots. Algorithm performance is evaluated through a series of experimental tests.

Notes

Sponsor: National Science Foundation

Associated Center(s) / Consortia: Quality of Life Technology Center

Text Reference

Jifu Zhou, Luis Ernesto Navarro-Serment, and Martial Hebert, "Detection of Parking Spots Using 2D Range Data," Proceedings of the 15th IEEE Intelligent Transportation Systems Conference 2012, September, 2012.

BibTeX Reference

@inproceedings{Zhou_2012_7279,
   author = "Jifu Zhou and Luis Ernesto Navarro-Serment and Martial Hebert",
   title = "Detection of Parking Spots Using 2D Range Data",
   booktitle = "Proceedings of the 15th IEEE Intelligent Transportation Systems Conference 2012",
   publisher = "IEEE",
   month = "September",
   year = "2012",
}

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