UAV Visual Navigation System based on Digital Twin

In recent years, many scholars have carried out researchs on UAV digital twin from various aspects. However, the research is still in the preliminary stage, and there are still some problems, such as incomplete data and model fusion, poor migration of algorithm policy, poor relation between virtual and physical space, and lack of extensibility of application scenarios. In order to explore the application potential of digital twin technology in UAV fields, this paper introduces digital twin into UAV monocular visual navigation. Therefore, this paper proposes a digital twin(DT)-based framework integrating with deep neural network, which consists of physical space, virtual space, twin data layer and application layer. Next, the multi-modal decision model with decoupling methods in application layer consisting of perception model and control model is built to explore the global optimal solution and control the behaviors of UAV. Finally, the digital twin system and decision model are verified in virtual space and physical space respectively. The results shows that the UAV visual navigation system based on digital twin reduces the cost of application, algorithm development and deployment, and improves the migration ability of navigation policy. Compared with the baselines, the proposed decision model has the best navigation performance in both virtual space and physical space. Compared with the navigation policy without the decoupling method, the performance index is improved by about 8.6% in virtual space and 2.7 times in physical space.