一种改进Delaunay三角剖分的临时道路检测方法

A temporary road detection method based on an improvedDelaunay triangulation

针对由交通锥桶引导的临时道路,提出一种改进Delaunay三角剖分算法,实现该特殊场景下的道路检测。使用YOLOv4算法对图像中的交通锥桶目标进行识别,并融合图像信息与激光雷达获取的交通锥桶点云信息,对融合后的交通锥桶信息进行Delaunay三角剖分,提出一种Delaunay三角网滤波算法与局部优化策略,根据路况变化实现Delaunay三角网权重与损失值的实时计算,有效滤除损失值总和不满足条件的三角边,算法减少了Delaunay三角网内的噪声约束,有效实现车道线与可行驶路径的快速规划与实时更新。实车实验结果表明:该算法平均耗时35.4 ms,所检测路径绝对轨迹误差为0.2 m、准确率为97%,相比传统Delaunay三角剖分算法,改进后的算法满足实时性要求,降低了路径检测误差,提高了路径检测准确率。

For temporary roads guided by traffic cones,this paper proposes an improved Delaunay triangulation algorithm to implement road detection in this special scene.YOLOv4 algorithm is used to recognize traffic cones in the images.Besides,the image information and the point cloud information of the traffic cones obtained by laser radar are fused,and the fused traffic cone information is Delaunay triangulated to propose a Delaunay triangulation filtering algorithm and the local optimization strategy,which calculates the weight and the loss value of the Delaunay triangulation in real time according to the change of road conditions and effectively filters out the triangular edges whose sum of the loss values fails to match the conditions.The proposed method reduces the noise constraint in the Delaunay triangulation and effectively implements the rapid planning and real-time updating of lane lines and drivable paths.The real vehicle experiment results show that the average time consumption of the proposed method is 35.4 ms.The absolute trajectory error of the detected path is 0.2 m,and the accuracy is 97%.Compared with the traditional Delaunay triangulation method,the improved method meets the real-time demand,reduces the path detection error,and improves the path detection accuracy.