Semantic Key-point Extraction Based Space Target Pose Estimation from Optical Image via Hourglass Network

Pose estimation for space target is an essential prerequisite for various space missions. The key part of target pose estimation is establishing the mapping relationship between the two-dimensional feature and three-dimensional feature quickly and accurately. This task usually be decomposed into two sequential steps where are feature extraction and feature correlation. However, high dynamic illumination variation and the relative high-speed movement characteristics of the space target will significantly decay the reliability of image feature extraction which will affect the accuracy of feature matching and finally cause a drop in the accuracy of the pose estimation of the space target in the reality scene of space observation. To solve this problem, this paper proposes a pose estimation method based on semantic key point. In this work, the Hourglass Network is used to extract the key points containing semantic information end-to-end, thereby, the mapping relationship between the two-dimensional feature and three-dimensional feature is established. Then the target pose can be estimated utilized the EPnP algorithm. Experimental results demonstrate that the method proposed in this paper can reach a compromise between the accuracy and efficiency of the algorithm. The minimal error of pose estimation on the simulation data set can reach 0.83°, and the average error is better than the traditional method in the case of data degradation.