基于卷积神经网络的非合作目标两阶段位姿估计方法

A two-stage relative pose estimation algorithm based on convolutional neural network was proposed to solve the problem of pose estimation for space noncooperative targets in orbit service. The detection module was combined with translation regression module in the first stage, and the detected image was input into stage two. An attitude estimation model was designed for flight around and flight approach during the mission. The indirect method of classification instead of regression was used in flying around, and the direct regression method was adopted to estimate the attitude when approaching, so as to realize pose estimation of noncooperative targets in orbit service process. A large-scale dataset is introduced, which can be utilized as a benchmark for pose estimation methods. Abundant ablation studies verified the effectiveness of each module. The position accuracy could reach 0.183 6 meters and attitude accuracy could reach 2.948 9 degrees, which shows the feasibility of monocular vision method based on convolutional neural network to estimate the pose of noncooperative targets in orbit service.