ChiNet：用于多模态航天器位姿估计的深度循环卷积学习
ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation
AI总结：
提出 ChiNet 深度循环卷积流程，融合 CNN、LSTM、RGB 与热红外信息及三阶段训练策略，实现航天器相对位姿估计并完成数据验证。
摘要：
本文提出了一种创新的深度学习流程，通过融合交会序列中的时间信息来估计航天器的相对位姿。该方法利用长短期记忆（LSTM）单元在数据序列建模方面的性能，处理由卷积神经网络（CNN）骨干提取的特征。研究采用三种不同的训练策略，并遵循由粗到精的漏斗式方法，将其结合起来以促进特征学习，并通过回归改善端到端位姿估计。该框架利用 CNN 从图像中自主确定特征表示的能力，将热红外数据与红绿蓝（RGB）输入相融合，从而缓解在可见光波段拍摄空间物体时伪影造成的影响。所提出框架名为 ChiNet，其每一项贡献都在合成数据集上进行了演示，完整流程也在实验数据上得到验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
