论文标题
无人机激光点云与深神经网络的堆栈交换
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks
论文作者
论文摘要
堆栈互换是运输系统的重要组成部分。移动激光扫描（MLS）系统已被广泛用于道路基础设施映射中，但是复杂多层堆栈互换的准确映射仍然具有挑战性。这项研究检查了由新的无人机（UAV）光检测和射程（LIDAR）系统收集的点云，以执行堆栈交换的语义分割任务。提出了一个端到端监督的3D深度学习框架，以对点云进行分类。所提出的方法已证明可以在复杂的互换场景中捕获3D特征，并堆叠卷积，结果达到了93％的分类精度。此外，新型的低成本半稳态雷达传感器Livox Mid-40具有不可限制的玫瑰花结扫描模式，已经证明了其在高清城市地图中的潜力。
Stack interchanges are essential components of transportation systems. Mobile laser scanning (MLS) systems have been widely used in road infrastructure mapping, but accurate mapping of complicated multi-layer stack interchanges are still challenging. This study examined the point clouds collected by a new Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) system to perform the semantic segmentation task of a stack interchange. An end-to-end supervised 3D deep learning framework was proposed to classify the point clouds. The proposed method has proven to capture 3D features in complicated interchange scenarios with stacked convolution and the result achieved over 93% classification accuracy. In addition, the new low-cost semi-solid-state LiDAR sensor Livox Mid-40 featuring a incommensurable rosette scanning pattern has demonstrated its potential in high-definition urban mapping.
