Moving object classification using horizontal laser scan data

Authors:
Huijing Zhao;Quanshi Zhang;Masaki Chiba;Ryosuke Shibasaki;Jinshi Cui;Hongbin Zha
Affiliations:
State Key Lab of Machine Perception, MOE, Peking University;State Key Lab of Machine Perception, MOE, Peking University;Technical Research Center, Mazda Motor Corporation;Center for Spatial Information Science, University of Tokyo;State Key Lab of Machine Perception, MOE, Peking University;State Key Lab of Machine Perception, MOE, Peking University
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

Motivated by two potential applications, i.e. enhancing driving safety and traffic data collection, a system has been developed using a single-layer horizontal laser scanner as the major sensor for both localization and perception of the surroundings in a large dynamic urban environment. This research focuses on a classification method, that given a stream of laser measurements, classify the moving object into either a person, a group of people, a bicycle or a car. In this research, a number of features are defined after examining the property of data appearance. A classification method is proposed after examining the likelihood measures between each pair of feature and class. Experimental results are presented, demonstrating that the algorithm has efficiency with respect to both driving safety and traffic data collection in highly dynamic environment.