A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters

Abstract

Navigation map generation based on remote sensing images is crucial in fields such as autonomous driving and geographic surveying. Style transfer is an effective method for obtaining a navigation map of the current environment. However, there is lack of robustness of the map-style transfer model, resulting in unsatisfactory quality of the generated navigation maps. To address these challenges, we average the parameters of generators sampled from different iterations with a dense sampling strategy in the Generative Adversarial Network (CycleGAN). The results demonstrate that the training efficiency of our method on the MNIST and generation quality on the Google Map dataset are significantly superior to traditional style transfer methods. Moreover, it performs well in multi-environment hybrid mapping. Our method improves the generalization ability of the model and converts existing navigation maps to other styles of maps precisely. It can better adapt to different types of urban layout and road planning, bringing innovative solutions for traffic management and navigation systems.