Pose estimation for monocular image object using convolution neural network

Abstract

Obtaining the accurate position and attitude of the object is the key to realize rendezvous and docking, on-orbit maintenance and other related tasks of space spacecraft. However, for non-cooperative objects, they lack prearranged cooperation sign, which would make it much more difficult to estimate their pose. Therefore, this paper attempts to use the powerful feature learning ability of neural network to establish the mapping relationship between the object in image and its current pose, then regresses the pose parameters of the object from a monocular image. Finally, we tested and verified the network on the public satellite dataset called Speed. The results showed that the translation error was 0.1237 and the rotation error was 0.1335.