基于多关键点检测加权融合的无人机相对位姿估计算法OA北大核心CSTPCD

A 6D target pose estimation algorithm based on multiple models keypoints weighted fusion is proposed to address the issue of low accuracy and poor robustness in obtaining the relative pose of unmanned aerial vehicles due to motion blur caused by the influence of water surface waves on the image during the landing phase of un-manned aerial vehicles.This algorithm aims to improve the accuracy and robustness of pose estimation.Firstly,based on the motion information obtained from the unmanned ship gyroscope,an inter frame jitter model is de-signed to reduce image noise by restoring image information.Then,a cascaded regression feature extraction al-gorithm with multiple models is designed to detect images obtained by the shipborne visual system through mul-tiple models,in order to enhance the diversity of the feature space;at the same time,the incremental set of keypo-int localization shapes during the detection process is used as the fusion weight to weight and fuse the model,in or-der to improve the robustness of the feature space.This paper uses efficient perspective-n-point(EPnP)to calcu-late the coordinates of the camera coordinate system for keypoints,and transforms the perspective-n-point(PnP)problem into an iterative closest point(ICP)problem.Finally,based on the dispersion of the keypoints solution set,weights are assigned to keypoints,and the ICP algorithm is used to mitigate the influence of depth information on the pose estimation.The simulation results show that this algorithm can establish a more accurate feature space,re-duce the loss of feature mapping during pose estimation,and ultimately improve the accuracy of pose estimation.