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http://dx.doi.org/10.7746/jkros.2012.7.2.092

Fast Scene Understanding in Urban Environments for an Autonomous Vehicle equipped with 2D Laser Scanners  

Ahn, Seung-Uk (한국과학기술원 로봇공학학제전공)
Choe, Yun-Geun (한국과학기술원 로봇공학학제전공)
Chung, Myung-Jin (한국과학기술원 전기및전자공학과)
Publication Information
The Journal of Korea Robotics Society / v.7, no.2, 2012 , pp. 92-100 More about this Journal
Abstract
A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.
Keywords
Semantic mapping; Scene Understanding; Sequential point Classification; Real-time Object Classification; Urban Environment;
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Times Cited By KSCI : 1  (Citation Analysis)
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