Fang Bin, Ma Jie, An Pei, Wang Zhao, Zhang Jun, Yu Kun
Appl Opt. 2021 May 10;60(14):4154-4164. doi: 10.1364/AO.419746.
The LiDAR sensor has been widely used for reconstruction in urban scenes. However, the current registration method makes it difficult to find stable 3D point correspondences from sparse and low overlapping LiDAR point clouds. In the urban situation, most of the LiDAR point clouds have a common flat ground. Therefore, we propose a novel, to the best of our knowledge, multi-level height (MH) maps-based coarse registration method. It requires that source and target point clouds have a common flat ground, which is easily satisfied for LiDAR point clouds in urban scenes. With MH maps, 3D registration is simplified as 2D registration, increasing the speed of registration. Robust correspondences are extracted in MH maps with different height intervals and statistic height information, improving the registration accuracy. The solid-state LiDAR Livox Mid-100 and mechanical LiDAR Velodyne HDL-64E are used in real-data and dataset experiments, respectively. Verification results demonstrate that our method is stable and outperforms state-of-the-art coarse registration methods for the sparse case. Runtime analysis shows that our method is faster than these methods, for it is non-iterative. Furthermore, our method can be extended for the unordered multi-view point clouds.
激光雷达传感器已被广泛应用于城市场景重建。然而，当前的配准方法难以从稀疏且重叠度低的激光雷达点云中找到稳定的三维点对应关系。在城市环境中，大多数激光雷达点云都有一个共同的平坦地面。因此，据我们所知，我们提出了一种基于多级高度（MH）地图的新型粗配准方法。它要求源点云和目标点云有一个共同的平坦地面，这对于城市场景中的激光雷达点云很容易满足。利用MH地图，三维配准被简化为二维配准，提高了配准速度。通过在不同高度区间的MH地图中提取稳健的对应关系并统计高度信息，提高了配准精度。分别在实际数据和数据集实验中使用了固态激光雷达Livox Mid-100和机械激光雷达Velodyne HDL-64E。验证结果表明，我们的方法是稳定的，并且在稀疏情况下优于现有的粗配准方法。运行时分析表明，我们的方法比这些方法更快，因为它是非迭代的。此外，我们的方法可以扩展到无序的多视点云。
