基于点云密度的结构化道路边界增强检测方法
发布时间：2018-11-10 19:39
【摘要】：为快速鲁棒地检测结构化道路边界,提出一种基于HDL-64E激光雷达点云密度的道路边界增强检测方法。通过建立虚拟雷达模型,利用点云密度特征,实现前景与背景分离,并利用随机采样一致性算法得到20m内的道路边界。为解决20~100m内道路边界点云稀疏、检测准确性下降的问题,提出利用光线切割模型对道路边界进行增强检测。在校园道路和城市快速路进行实验,道路边界检测率达到95%以上,有效检测距离可达70m以上,检测周期小于32ms。
[Abstract]:In order to quickly and robustly detect structured road boundary, a method of road boundary enhancement detection based on point cloud density of HDL-64E lidar is proposed. Based on the virtual radar model and the feature of point cloud density, the foreground is separated from the background, and the road boundary within 20m is obtained by random sampling consistency algorithm. In order to solve the problem that road boundary cloud is sparse and the accuracy of detection is decreased within 20 ~ 100m, an enhanced detection method based on optical wire cutting model is proposed. Experiments on campus road and urban expressway show that the detection rate of road boundary is more than 95%, the effective detection distance is more than 70 m, and the detection period is less than 32 Ms.
【作者单位】： 军事交通学院研究生管理大队;军事交通学院军用车辆系;
【基金】：国家自然科学基金(91220301) 国家重点基础研发计划项目(2016YFB0100903)资助
【分类号】：TN958.98
[Abstract]:In order to quickly and robustly detect structured road boundary, a method of road boundary enhancement detection based on point cloud density of HDL-64E lidar is proposed. Based on the virtual radar model and the feature of point cloud density, the foreground is separated from the background, and the road boundary within 20m is obtained by random sampling consistency algorithm. In order to solve the problem that road boundary cloud is sparse and the accuracy of detection is decreased within 20 ~ 100m, an enhanced detection method based on optical wire cutting model is proposed. Experiments on campus road and urban expressway show that the detection rate of road boundary is more than 95%, the effective detection distance is more than 70 m, and the detection period is less than 32 Ms.
【作者单位】： 军事交通学院研究生管理大队;军事交通学院军用车辆系;
【基金】：国家自然科学基金(91220301) 国家重点基础研发计划项目(2016YFB0100903)资助
【分类号】：TN958.98
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