  This paper presents an innovative deep learning pipeline which estimates the
relative pose of a spacecraft by incorporating the temporal information from a
rendezvous sequence. It leverages the performance of long short-term memory
(LSTM) units in modelling sequences of data for the processing of features
extracted by a convolutional neural network (CNN) backbone. Three distinct
training strategies, which follow a coarse-to-fine funnelled approach, are
combined to facilitate feature learning and improve end-to-end pose estimation
by regression. The capability of CNNs to autonomously ascertain feature
representations from images is exploited to fuse thermal infrared data with
red-green-blue (RGB) inputs, thus mitigating the effects of artefacts from
imaging space objects in the visible wavelength. Each contribution of the
proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and
the complete pipeline is validated on experimental data.
