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This paper presents an innovative deep learning pipeline which estimates therelative pose of a spacecraft by incorporating the temporal information from arendezvous sequence. It leverages the performance of long short-term memory(LSTM) units in modelling sequences of data for the processing of featuresextracted by a convolutional neural network (CNN) backbone. Three distincttraining strategies, which follow a coarse-to-fine funnelled approach, arecombined to facilitate feature learning and improve end-to-end pose estimationby regression. The capability of CNNs to autonomously ascertain featurerepresentations from images is exploited to fuse thermal infrared data withred-green-blue (RGB) inputs, thus mitigating the effects of artefacts fromimaging space objects in the visible wavelength. Each contribution of theproposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, andthe complete pipeline is validated on experimental data.

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation