# ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

arXiv: https://arxiv.org/abs/2108.10282 · HTML: https://mlsanity.com/paper/2108.10282/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 62 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [Satellite Pose Estimation Challenge: Dataset, Competition Design and Results](https://mlsanity.com/paper/1911.02050/) (2019)
- [Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement](https://mlsanity.com/paper/1908.11542/) (2019)
- [Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations](https://mlsanity.com/paper/1606.01305/) (2016)
- [Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering](https://mlsanity.com/paper/1907.04298/) (2019)
- [VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem](https://mlsanity.com/paper/1701.08376/) (2017)
- [Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks](https://mlsanity.com/paper/1809.07238/) (2018)
- [Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft](https://mlsanity.com/paper/1909.00392/) (2019)
- [LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network](https://mlsanity.com/paper/2104.09248/) (2021)
- [Robust On-Manifold Optimization for Uncooperative Space Relative Navigation with a Single Camera](https://mlsanity.com/paper/2005.07110/) (2020)
- [Using Convolutional Neural Networks for Relative Pose Estimation of a Non-Cooperative Spacecraft with Thermal Infrared Imagery](https://mlsanity.com/paper/2105.13789/) (2021)

## Built on by (most cited first, 11 of 11)

- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation](https://mlsanity.com/paper/2304.07728/) (2023)
- [Domain Generalization for In-Orbit 6D Pose Estimation](https://mlsanity.com/paper/2406.11743/) (2024)
- [Motion Aware ViT-based Framework for Monocular 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.06000/) (2025)
- [NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.14890/) (2025)
- [Orbital AI-based Autonomous Refuelling Solution](https://mlsanity.com/paper/2309.11648/) (2023)
- [Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous](https://mlsanity.com/paper/2311.05992/) (2023)
- [SpY: A Context-Based Approach to Spacecraft Component Detection](https://mlsanity.com/paper/2406.18709/) (2024)
- [TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2605.31217/) (2026)
- [HAT: Hypothesis-Anchored Tracking for Video Monocular Spacecraft Pose Estimation](https://mlsanity.com/paper/2609.21597/) (2026)

## Abstract (arXiv)

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.