Cooperative Perception for Digital Twin Reconstruction*

This work introduces an automated system for generating digital twins of urban environments by integrating data from multiple sensors, including a mobile vehicle equipped with various cameras and a LiDAR sensor, as well as strategi-cally placed stationary cameras. Through cooperative perception across these devices, the system improves the accuracy and precision of data capture. To achieve a detailed 3D reconstruction, we employ a hybrid SLAM and mapping approach that produces a dense point cloud. This preliminary point cloud is further refined with data from the stationary sensors using the FGICP point cloud registration method. The resulting digital twin produced by this pipeline are directly fine-tuned and have versatile applications across fields such as urban simulation and planning, cultural heritage preservation, traffic analysis, and more.