  Accurate real-time pose estimation of spacecraft or object in space is a key
capability necessary for on-orbit spacecraft servicing and assembly tasks. Pose
estimation of objects in space is more challenging than for objects on Earth
due to space images containing widely varying illumination conditions, high
contrast, and poor resolution in addition to power and mass constraints. In
this paper, a convolutional neural network is leveraged to uniquely determine
the translation and rotation of an object of interest relative to the camera.
The main idea of using CNN model is to assist object tracker used in on space
assembly tasks where only feature based method is always not sufficient. The
simulation framework designed for assembly task is used to generate dataset for
training the modified CNN models and, then results of different models are
compared with measure of how accurately models are predicting the pose. Unlike
many current approaches for spacecraft or object in space pose estimation, the
model does not rely on hand-crafted object-specific features which makes this
model more robust and easier to apply to other types of spacecraft. It is shown
that the model performs comparable to the current feature-selection methods and
can therefore be used in conjunction with them to provide more reliable
estimates.
