# Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU

arXiv: https://arxiv.org/abs/2204.03296 · HTML: https://mlsanity.com/paper/2204.03296/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [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)
- [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators](https://mlsanity.com/paper/2004.14525/) (2020)
- [Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering](https://mlsanity.com/paper/1907.04298/) (2019)
- [Wide-Depth-Range 6D Object Pose Estimation in Space](https://mlsanity.com/paper/2104.00337/) (2021)
- [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)
- [Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks](https://mlsanity.com/paper/1906.09868/) (2019)
- [Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery](https://mlsanity.com/paper/2101.09553/) (2021)
- [A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery](https://mlsanity.com/paper/2101.05661/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC](https://mlsanity.com/paper/2407.06170/) (2024)
- [Computer vision tasks for intelligent aerospace missions: An overview](https://mlsanity.com/paper/2407.06513/) (2024)
- [Exploration of TPUs for AI Applications](https://mlsanity.com/paper/2309.08918/) (2023)

## Abstract (arXiv)

Pose estimation of an uncooperative space resident object is a key asset towards autonomy in close proximity operations. In this context monocular cameras are a valuable solution because of their low system requirements. However, the associated image processing algorithms are either too computationally expensive for real time on-board implementation, or not enough accurate. In this paper we propose a pose estimation software exploiting neural network architectures which can be scaled to different accuracy-latency trade-offs. We designed our pipeline to be compatible with Edge Tensor Processing Units to show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space. The neural networks were tested both on the benchmark Spacecraft Pose Estimation Dataset, and on the purposely developed Cosmo Photorealistic Dataset, which depicts a COSMO-SkyMed satellite in a variety of random poses and steerable solar panels orientations. The lightest version of our architecture achieves state-of-the-art accuracy on both datasets but at a fraction of networks complexity, running at 7.7 frames per second on a Coral Dev Board Mini consuming just 2.2W.