Abstract
Since NASA put forward the concept of the digital twin in 2010, many industries have put forward the dynamic goal of digital development, and the transportation industry is also among them. With more and more companies laying out on this virgin land, the digital twin transportation industry has grown rapidly and gradually formed a complete scientific research system. However, under the largely mature framework, there are still many loophole problems that need to be solved. In the process of constructing a road network with point cloud information, we summarize several major features of the point cloud collected by laser scanners and analyze the potential problems of constructing the network, such as misjudging the feature points as ground points and grid voids. On this basis, we reviewed relevant literature and proposed targeted solutions, such as building a point cloud pyramid modeled after the image pyramid, expanding the virtual grid, etc., applying CSF for ground-point cloud extraction, and constructing a road network model using the PTD (progressive density-based filter) algorithm. For the problem of road sign detection, we optimize the remote sensing data in the ground point cloud by enhancing the information density using edge detection, improving the data quality by removing the low intensity points, and achieving 90% accuracy of road text recognition using PaddleOCR and Densenet. As for the real-time digital twin traffic, we design the P2PRN network using the backbone of MPR-GAN for 2D feature generation and SuperGlue for 2D feature matching, rendering the viewpoints according to the matching optimization points, completing the multimodal matching task after several iterations, and successfully calculating the road camera position with 10° and 15m accuracy.
Abstract (translated)
自2010年以来，由于NASA提出了数字孪生的概念，许多行业都提出了数字化发展的动态目标，交通运输业也不例外。随着越来越多的公司将目光投向这一片未开发的土地，数字孪生运输行业快速发展并逐渐形成了一个完整的科学研究系统。然而，在成熟的大框架下，仍然存在许多需要解决的问题。在用点云信息构建道路网络的过程中，我们总结了激光扫描器收集的点云的主要特点，并分析了构建网络的可能问题，例如错误地将特征点判断为地面点和网格空白。在此基础上，我们回顾了相关文献，提出了针对性的解决方案，例如建立一个像图像金字塔一样的点云金字塔模型，扩大虚拟网格等。应用CSF进行地面点云提取，并使用PTD（渐进密度滤波）算法构建道路网络模型。对于道路标志检测问题，我们通过增强边缘检测来提高地面点云中的信息密度，通过消除低强度点来提高数据质量，使用PaddleOCR和Densenet实现90%的道路文本识别准确率。对于实时数字孪生交通，我们使用MPR-GAN的骨干网络进行2D特征生成，使用SuperGlue进行2D特征匹配，根据匹配优化点计算观点，完成多次迭代后成功计算出道路摄像机位置，实现10°和15m的精度。
URL
https://arxiv.org/abs/2310.14296
