车载激光点云中的高速路面快速检测
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摘要
车载移动测图技术凭借高效率的地理信息数据获取能力,已经成为城市道路环境的主要调查手段。针对高速道路场景的移动激光扫描数据,现有的点云特征提取算法缺乏普遍适用性。为此,提出一种稳健的路面自动提取算法。首先,整个点云沿着轨迹方向分段,并使用欧氏距离滤波器移除噪点;接着,每个分段运用一种高效随机采样一致性算法来检测参数化的路平面;最后,候选路面点投影到水平面以生成点位图,并运用图像连通区域分析移除来自植被的异常值。实验证明,本方法可以从海量车载点云中高效提取路面点,提取准确度达到99.74%,满足应用需求。
引文
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[3]李英成,文沃根,王伟,等.快速获取地面三维数据的LIDAR技术系统[J].测绘科学,2002,27(4):35-38.
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[5]Manandhar D,Shibasaki R.Feature extraction from range data[C]∥2001:346-356.
[6]Munoz D,Vandapel N,Hebert M.Directional Associative Markov Network for 3-D Point Cloud Classification[J].In:International Symposium on 3-D Data Processing,Visualization,and Transmission,2009.
[7]Pu S,Rutzinger M,Vosselman G,et al.Recognizing basic structures from mobile laser scanning data for road inventory studies[J].Isprs Journal of Photogrammetry&Remote Sensing,2011,66(6):S28-S39.
[8]Anttoni J,Juha H,Hannu H,et al.Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping[J].Sensors,2008,8(9):5238.
[9]Elhinney C P M,Kumar P,Cahalane C,et al.Initial results from European Road Safety Inspection(EURSI)mobile mapping project[C]∥ISPRS Commission V Technical Symposium.2010.
[10]Yang B,Fang L,Li J.Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds[J].Isprs Journal of Photogrammetry&Remote Sensing,2013,79(5):80-93.
[11]Schnabel R,Wahl R,Klein R.Efficient RANSAC for Point-Cloud Shape Detection[J].Computer Graphics Forum,2007,26(2):214-226.