# Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC

arXiv: https://arxiv.org/abs/2409.12939 · HTML: https://mlsanity.com/paper/2409.12939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [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)
- [Accelerating Deep Learning Applications in Space](https://mlsanity.com/paper/2007.11089/) (2020)
- [GPU4S: Embedded GPUs in Space -- Latest Project Updates](https://mlsanity.com/paper/2109.11074/) (2021)

## Abstract (arXiv)

The challenging deployment of Artificial Intelligence (AI) and Computer Vision (CV) algorithms at the edge pushes the community of embedded computing to examine heterogeneous System-on-Chips (SoCs). Such novel computing platforms provide increased diversity in interfaces, processors and storage, however, the efficient partitioning and mapping of AI/CV workloads still remains an open issue. In this context, the current paper develops a hybrid AI/CV system on Intel's Movidius Myriad X, which is an heterogeneous Vision Processing Unit (VPU), for initializing and tracking the satellite's pose in space missions. The space industry is among the communities examining alternative computing platforms to comply with the tight constraints of on-board data processing, while it is also striving to adopt functionalities from the AI domain. At algorithmic level, we rely on the ResNet-50-based UrsoNet network along with a custom classical CV pipeline. For efficient acceleration, we exploit the SoC's neural compute engine and 16 vector processors by combining multiple parallelization and low-level optimization techniques. The proposed single-chip, robust-estimation, and real-time solution delivers a throughput of up to 5 FPS for 1-MegaPixel RGB images within a limited power envelope of 2W.