基于L1中轴提取和柔性约束的三维点云场景路网提取
Road Network Extraction of 3D Point Cloud Scene Based on L1-medial Extraction and Flexible Constraints
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摘要: 三维路网为城市交通优化、自动驾驶导航以及灾害应急响应等提供了关键的道路信息. 然而, 由于环境的复杂性和多样性, 三维路网的获取十分困难. 激光雷达点云作为一种被广泛使用的三维数据, 存在着无组织性且噪声较高的问题, 这给三维路网的提取带来了巨大挑战. 本文发现, 基于三维骨架提取的方法可以有效降低路网提取的复杂性. 因此, 本文首先采用L1骨架提取方法提取初始的三维骨架. 同时, 对于地面点云, 本文采用对称性方法构建质心距离场, 并利用该距离场对地面点云进行腐蚀, 以约束道路的中轴区域. 接着, 本文对初始路网骨架的断尾点进行判断并对路网进行补全. 最后通过柔性投影, 获得三维道路点云的最佳中轴位置. 本文通过大量实验对提取的三维路网进行了评估, 结果表明该方法能够有效提取出复杂三维道路的路网信息, 并相较于二维路网具有显著优势.Abstract: The 3D road network is crucial for urban traffic optimization, autonomous driving navigation, and disaster emergency response planning. However, obtaining it is highly challenging due to the complex and diverse environment. LiDAR point clouds, commonly used as a representation of 3D data, often suffer from disorganization and high noise levels, posing significant obstacles to road network extraction. This study proposes a method that effectively reduces the complexity of road network extraction by utilizing the L1 skeleton extraction method. Additionally, a symmetry-based approach is employed to construct a centroid distance field for the ground point cloud, facilitating erosion and constraining the central axis region of the road. The study detects the tail points of the initial road network skeleton and completes the road network based on this detection. Finally, flexible projection is used to determine the optimal central axis positions. Extensive experiments are conducted to evaluate the extracted 3D road network, demonstrating its effectiveness in extracting road network information from complex 3D roads and highlighting its significant advantages over 2D road networks.