Panoramic mapping and interaction technology between real-world images and power grid real-world twins

This article proposes a panoramic mapping and interaction method for real-world images and power grid real-world twins. The data of the power grid scene is obtained through high-precision data acquisition technology, and accurate positioning is carried out. Secondly, the collected point cloud data is optimized and processed to construct a power grid real-world twin model, and texture mapping is performed. The panoramic mapping algorithm is used to map the power grid real-world twin model to the real-world image. Real time object detection and tracking, attitude estimation, and visual feedback and effect presentation are performed. The YOLO object detection algorithm and Kalman filter technology are used to achieve target detection, tracking, and state estimation. Finally, the Extended Kalman Filter (EKF) algorithm is used to reflect the changes in the real power grid, and it is inputted as an observation value. In EKF, through the prediction and update steps of EKF, Integrate the state of the model and sensor observations to perform incremental updates on the dynamic model.