首页|Absolute pose estimation of UAV based on large-scale satellite image
Absolute pose estimation of UAV based on large-scale satellite image
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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.
Satellite imageryUnmanned Aerial Vehicle(UAV)Deep neural networksVision navigationvon Mises-Fisher distribution
Hanyu WANG、Qiang SHEN、Zilong DENG、Xinyi CAO、Xiaokang Wang
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School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China
Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401120,China
National Natural Science Foundation of ChinaChongqing Natural Science Foundation,China
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NSTL
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