面向车载激光扫描数据的道路目标精细化鲁棒提取

Refining and Robust Extraction of Roads from Mobile Laser Scanning Point Clouds

精确的三维道路信息,对于交通运输、城市规划、道路网建设、三维道路建模等具有重要意义。车载移动测量系统作为一项高新测绘技术能快速、准确地获取三维激光点云,适用于大场景的道路提取与建模。本文提出了一种从车载激光点云中快速精细化提取三维道路及其边界的方法。该方法首先利用车载激光点云的空间特征对点云进行自适应分段,然后利用先验知识与规则提取候选的道路及其边界,并根据道路边界的线状特征,对所提取的道路边界进行跟踪及矢量化,最后得到道路的参数。为了验证本方法的有效性,试验采用了3组不同道路场景的数据进行验证,结果表明提取结果的准确度、完整度及检测质量都达到了90%以上。定量与定性的结果分析表明,本文方法对于不同复杂场景下不同点密度的数据具有很好的适应性,能快速、鲁棒地提取大场景内的道路及其边界。

Accurate three- dimensional road information has important significance in the fields of transportation, urban planning,road network construction, 3D road modeling and intelligent vehicle. For instance, in the field of intelligent vehicle, accurate threedimensional road information can provide lane level road information for autonomous navigation. As a high-tech tool of surveying and mapping, mobile laser scanning can obtain 3D laser point cloud quickly and accurately and is suitable for road extraction and modeling in large- scale scenes. A robust method for extracting the refined three- dimensional road and its boundary from mobile laser scanning point clouds is proposed. First, the point cloud is self-adaptively partitioned according to the spatial feature of mobile laser point cloud. Then, the candidate road and its boundary are extracted based on the prior knowledge and rules. And the extracted road boundary is tracked and vectorized according to the linear feature of the road boundary. Finally, the road model parameters are computed by the extracted road and its boundary. Experiments were undertaken to evaluate the validities of the proposed method with three different scene datasets, including the highway, urban area and campus. The highway dataset contains a steep ramp and its point density is low, the urban dataset contains flower beds in the middle of the road, the campus dataset contains a great many of objects such as trees, pedestrians, cars et al and its point density is very high. The completeness, correctness, and quality of the extracted roads are over 91.29%, 93.15%, and 90.08%, respectively, which proves the proposed method is capable in various complex scenes with different point density, which is fast and robust to extract road and its boundary in large-scale scenes.