ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation
This addresses the problem of accurate spacecraft pose estimation for space rendezvous missions, though it appears incremental as it builds on existing CNN and LSTM methods.
The paper tackles spacecraft pose estimation by developing ChiNet, a deep learning pipeline that fuses RGB and thermal infrared data using a CNN-LSTM architecture with coarse-to-fine training, achieving improved pose regression validated on synthetic and experimental datasets.
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.