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.
Zhou et al. used AdaBoost algorithm to train a classifier on 2-D laser scans, and extracted car bumpers as main features of parked vehicles REF .
Detection of parking spots using 2D range data
2012 15th International IEEE Conference on Intelligent Transportation Systems
2012 15th International IEEE Conference on Intelligent Transportation Systems
Engineering Computer Science