# Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1809.07238 · HTML: https://mlsanity.com/paper/1809.07238/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 30 of 34)

- [SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap](https://mlsanity.com/paper/2110.03101/) (2021)
- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022)
- [Deep Learning-based Spacecraft Relative Navigation Methods: A Survey](https://mlsanity.com/paper/2108.08876/) (2021)
- [Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery](https://mlsanity.com/paper/2101.09553/) (2021)
- [CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space](https://mlsanity.com/paper/2207.07869/) (2022)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [Spacecraft Pose Estimation Based on Unsupervised Domain Adaptation and on a 3D-Guided Loss Combination](https://mlsanity.com/paper/2212.13415/) (2022)
- [ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation](https://mlsanity.com/paper/2108.10282/) (2021)
- [6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics](https://mlsanity.com/paper/2303.13241/) (2023)
- [Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution](https://mlsanity.com/paper/2308.14666/) (2023)
- [Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations](https://mlsanity.com/paper/2405.12728/) (2024)
- [SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment](https://mlsanity.com/paper/2104.05978/) (2021)
- [Occlusion-Robust Object Pose Estimation with Holistic Representation](https://mlsanity.com/paper/2110.11636/) (2021)
- [Bridging the Domain Gap for Flight-Ready Spaceborne Vision](https://mlsanity.com/paper/2409.11661/) (2024)
- [Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation](https://mlsanity.com/paper/2205.02065/) (2022)
- [Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization](https://mlsanity.com/paper/2507.19459/) (2025)
- [Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU](https://mlsanity.com/paper/2204.03296/) (2022)
- [3D Visual Tracking Framework with Deep Learning for Asteroid Exploration](https://mlsanity.com/paper/2111.10737/) (2021)
- [Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation](https://mlsanity.com/paper/2409.06240/) (2024)
- [A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery](https://mlsanity.com/paper/2101.05661/) (2021)
- [SU-Net: Pose estimation network for non-cooperative spacecraft on-orbit](https://mlsanity.com/paper/2302.10602/) (2023)
- [NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimation](https://mlsanity.com/paper/2509.14890/) (2025)
- [Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge](https://mlsanity.com/paper/2512.23998/) (2025)
- [Markers Identification for Relative Pose Estimation of an Uncooperative Target](https://mlsanity.com/paper/2407.20515/) (2024)
- [Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis](https://mlsanity.com/paper/2410.04298/) (2024)
- [Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflows](https://mlsanity.com/paper/2503.08700/) (2025)
- [Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting](https://mlsanity.com/paper/2507.05698/) (2025)
- [Differentiable Rendering for Pose Estimation in Proximity Operations](https://mlsanity.com/paper/2212.12668/) (2022)
- [Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous](https://mlsanity.com/paper/2311.05992/) (2023)

## Abstract (arXiv)

On-board estimation of the pose of an uncooperative target spacecraft is an essential task for future on-orbit servicing and close-proximity formation flying missions. However, two issues hinder reliable on-board monocular vision based pose estimation: robustness to illumination conditions due to a lack of reliable visual features and scarcity of image datasets required for training and benchmarking. To address these two issues, this work details the design and validation of a monocular vision based pose determination architecture for spaceborne applications. The primary contribution to the state-of-the-art of this work is the introduction of a novel pose determination method based on Convolutional Neural Networks (CNN) to provide an initial guess of the pose in real-time on-board. The method involves discretizing the pose space and training the CNN with images corresponding to the resulting pose labels. Since reliable training of the CNN requires massive image datasets and computational resources, the parameters of the CNN must be determined prior to the mission with synthetic imagery. Moreover, reliable training of the CNN requires datasets that appropriately account for noise, color, and illumination characteristics expected in orbit. Therefore, the secondary contribution of this work is the introduction of an image synthesis pipeline, which is tailored to generate high fidelity images of any spacecraft 3D model. The proposed technique is scalable to spacecraft of different structural and physical properties as well as robust to the dynamic illumination conditions of space. Through metrics measuring classification and pose accuracy, it is shown that the presented architecture has desirable robustness and scalable properties.