GPS availability prediction based on air-ground collaboration
Zun Liu, Wenlian Huang, Yanyan Chen, Jie Chen · International Journal of Space-Based and Situated Computing · 2023
Robots such as unmanned aerial vehicles (UAVs) have been widely applied in emergency rescue scenes. However, there is a lack of distribution of ground GPS signals in the complex environment, which indirectly affects the take-off and landing of UAVs. To solve this problem, we have proposed an air-ground collaborative mapping system based on the Gaussian Process (GP) and convolutional neural network (CNN). Firstly, CNN is used to predict whether the GPS signals are available. And the GP is used to interpolate and predict the areas not visited by the UAVs, then the GPS signal distribution map is obtained. Compared with the traditional mapping methods, the system does not require size parameters and can build maps more efficiently and quickly.