Abstract
Obtaining absolute pose based on pre-loaded satellite images is one of the important means of autonomous navigation for small Unmanned Aerial Vehicles(UAVs)in Global Naviga-tion Satellite System(GNSS)denied environments.Most of the previous works have tended to build Convolutional Neural Networks(CNNs)to extract features and then directly regress the pose,which will fail when solving the challenges caused by the huge viewpoint and size differences between"UAV-satellite"image pairs in real-world scenarios.Therefore,this paper proposes a probability distribution/regression integrated deep model with the attention-guided triple fusion mechanism,which estimates discrete distributions in pose space and three-dimensional vectors in translation space.In order to overcome the shortage of the relevant dataset,this paper simulates image datasets captured by UAVs with forward-facing cameras during target searching and auton-omous attacking.The effectiveness,superiority,and robustness of the proposed method are verified by simulated datasets and flight tests.
基金项目
National Natural Science Foundation of China(61973033)
Chongqing Natural Science Foundation,China(cstc2021jcyjmsxmX0737)
NETL
NSTL
万方数据