Pose Estimation for Non-cooperative Spacecraft based on Deep Learning
作者单位：School of Automation Science and Electrical Engineering Beihang University Shanghai Aerospace Control Technology Institute Beijing Advanced Innovation Center for Big-Data Based Precision Medicine Beihang University
会议名称：《第三十九届中国控制会议》
会议日期：2020年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081105[工学-导航、制导与控制] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Non-cooperative Spacecraft Pose Estimation Convolution Neural Network Geometry Optimization
摘 要：The pose estimation of non-cooperative space-borne object is of vital importance for on-orbit service and spacecraft approaching missions. Based on images taken by a monocular camera, an estimate algorithm is proposed to estimate the relative position and the relative attitude of non-cooperative spacecraft. The approach utilizes the off-the-shelf target detection network and key point regression network to predict 2D key points coordinates and combines the multiple view triangulation to reconstruct 3D model. Nonlinear least squares method is used to minimize 2D-3D corresponding coordinates to predict position and attitude. The proposed method effectively combines deep learning and geometric optimization algorithms, which is an innovative application of deep learning in the aerospace field. Finally, simulations are performed to prove the effectiveness of the theoretical results.