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Semi-Automated Generation of Road Transition Lines Using Mobile Laser Scanning Data

机译:使用移动激光扫描数据半自动地产生道路过渡线路

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摘要

This paper recognizes the research gaps and difficulties in generating transition lines (the paths that pass through a road intersection) in road intersections from mobile laser scanning (MLS) point clouds. The proposed method contains three modules: road surface detection, lane marking extraction, and transition line generation. First, the points covering the road surface are extracted using the voxel-based upward growing and the improved region growing. Then, lane markings are extracted and identified according to the multi-thresholding and the geometric filtering. Finally, transition lines are generated through a combination of the lane node structure generation algorithm and the cubic Catmull-Rom spline algorithm. The experimental results demonstrate that transition lines can be successfully generated for both T- and cross-intersections with promising accuracy. In the validation of lane marking extraction using the manually interpreted lane marking points, the method can achieve average precision, recall, and F-1-score of 90.80%, 92.07%, and 91.43%, respectively. The success rate of transition line generation is 96.5%. Furthermore, the buffer-overlay-statistics (BOS) method validates that the proposed method can generate lane centerlines and transition lines within 20-cm-level localization accuracy from the MLS point clouds.
机译:本文认识到在流动激光扫描(MLS)点云的道路交叉路口中产生过渡线(通过道路交叉路口的路径)的研究差距和困难。所提出的方法包含三个模块:道路表面检测,车道标记提取和过渡线。首先,利用基于体素的向上生长和改进的区域生长来提取覆盖道路表面的点。然后,根据多阈值和几何滤波提取和识别出线路标记。最后,通过车道节点结构生成算法和立方Catmull-ROM样条算法的组合生成过渡线。实验结果表明,可以针对具有有希望的精度的T-和交叉点来成功生成过渡线。在使用手动解释的车道标记点的车道标记提取的验证中,该方法可以分别达到平均精度,召回和F-1分别为90.80%,92.07%和91.43%。过渡线生成的成功率为96.5%。此外,缓冲覆盖统计(BOS)方法验证所提出的方法可以从MLS点云从MLS点云产生20cm级定位精度的通道中心线和转换线。

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