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{\deg} and 15m accuracy.

构建一个包含数字孪生、交通行业、点云、道路网络和道路标志检测的研究论文的关键词和总结。