Monocular pose estimation is the challenge of estimating the relative attitude and position of a target using a single camera. The computer vision community has demonstrated high accuracy and robust algorithms for pose estimation in terrestrial applications. However the space environment presents unique challenges. Space-grade hardware for example greatly constrains the power consumption and memory bandwidth of such algorithms. As such, the computer science trend of larger and larger networks is not feasible in a deployed hardware application.
Master’s project (can be adapted for TP-IV)
Objective
The aim of this project is to demonstrate spacecraft pose estimation algorithms running on small edge devices.
Tasks
Survey available pose estimation algorithms; select two contenders.
Deploy the algorithms to an edge device (currently a JETSON NANO is available).
Compare performance of the two algorithms.
- latency
- memory
- accuracy
Prerequisites
- Proficiency in python
- Familiarity with pytorch
- Understanding of the camera model (world frame to image plane linear algebra)
- Experience with micro-controllers or edge-devices a plus
References
https://arxiv.org/abs/2305.07348
Acta Astronautica 2023
Wide-Depth-Range 6D Object Pose Estimation in Space
https://arxiv.org/abs/2104.00337
CVPR 2021
DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses
https://arxiv.org/pdf/2403.13683
CVPR 2024
Contact
This project will be conducted in collaboration with CVLab. Contact [email protected] for more information.