Sensors 2015, 15(9), 21931-21956; https://doi.org/10.3390/s150921931
A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment
1
Department of Automation, University of Science and Technology of China, Hefei 230026, China
2
Institute of Applied Technology , Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 23 June 2015 / Revised: 16 August 2015 / Accepted: 24 August 2015 / Published: 31 August 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)
Abstract
The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing. View Full-TextKeywords:
dynamic obstacle modeling; multi-beam LIDAR; multi-feature ground segmentation; road curb modeling
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Liu, J.; Liang, H.; Wang, Z.; Chen, X. A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment. Sensors 2015, 15, 21931-21956.