# Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks

arXiv: https://arxiv.org/abs/1906.09868 · HTML: https://mlsanity.com/paper/1906.09868/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 21 of 21)

- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization](https://mlsanity.com/paper/2109.03349/) (2021)
- [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022)
- [Dynamical Pose Estimation](https://mlsanity.com/paper/2103.06182/) (2021)
- [Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation](https://mlsanity.com/paper/2205.02065/) (2022)
- [Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU](https://mlsanity.com/paper/2204.03296/) (2022)
- [FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation](https://mlsanity.com/paper/2512.09792/) (2025)
- [Using Convolutional Neural Networks for Relative Pose Estimation of a Non-Cooperative Spacecraft with Thermal Infrared Imagery](https://mlsanity.com/paper/2105.13789/) (2021)
- [Domain Generalization for In-Orbit 6D Pose Estimation](https://mlsanity.com/paper/2406.11743/) (2024)
- [Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation](https://mlsanity.com/paper/2409.06240/) (2024)
- [Spiking monocular event based 6D pose estimation for space application](https://mlsanity.com/paper/2501.02916/) (2025)
- [Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC](https://mlsanity.com/paper/2407.06170/) (2024)
- [SpaceSense-Bench: A Large-Scale Multi-Modal Benchmark for Spacecraft Perception and Pose Estimation](https://mlsanity.com/paper/2603.09320/) (2026)
- [Computer vision tasks for intelligent aerospace missions: An overview](https://mlsanity.com/paper/2407.06513/) (2024)
- [Segmentation-based Detection for Efficient Multi-Task Spacecraft Perception](https://mlsanity.com/paper/2606.15409/) (2026)
- [Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects](https://mlsanity.com/paper/2501.13009/) (2025)
- [Enabling Robust, Real-Time Verification of Vision-Based Navigation through View Synthesis](https://mlsanity.com/paper/2507.02993/) (2025)
- [Digital and Robotic Twinning for Validation of Proximity Operations and Formation Flying](https://mlsanity.com/paper/2507.20034/) (2025)
- [Sun sensor calibration algorithms: A systematic mapping and survey](https://mlsanity.com/paper/2507.21541/) (2025)
- [TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation](https://mlsanity.com/paper/2605.31217/) (2026)
- [GAP-GDRNet: Geometry-aware monocular 6D pose estimation for spacecraft using synthetic geometric supervision](https://mlsanity.com/paper/2607.02360/) (2026)

## Abstract (arXiv)

This work introduces the Spacecraft Pose Network (SPN) for on-board estimation of the pose, i.e., the relative position and attitude, of a known non-cooperative spacecraft using monocular vision. In contrast to other state-of-the-art pose estimation approaches for spaceborne applications, the SPN method does not require the formulation of hand-engineered features and only requires a single grayscale image to determine the pose of the spacecraft relative to the camera. The SPN method uses a Convolutional Neural Network (CNN) with three branches to solve for the pose. The first branch of the CNN bootstraps a state-of-the-art object detector to detect a 2D bounding box around the target spacecraft. The region inside the bounding box is then used by the other two branches of the CNN to determine the attitude by initially classifying the input region into discrete coarse attitude labels before regressing to a finer estimate. The SPN method then uses a novel Gauss-Newton algorithm to estimate the position by using the constraints imposed by the detected 2D bounding box and the estimated attitude. The secondary contribution of this work is the generation of the Spacecraft PosE Estimation Dataset (SPEED). SPEED consists of synthetic as well as actual camera images of a mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images are created by fusing OpenGL-based renderings of the spacecraft's 3D model with actual images of the Earth captured by the Himawari-8 meteorological satellite. The actual camera images are created using a 7 degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of the Tango spacecraft. The SPN method, trained only on synthetic images, produces degree-level attitude error and cm-level position errors when evaluated on the actual camera images not used during training.