Mobile-URSONet: an embeddable Neural Network for Spacecraft Pose Estimation

Pytorch implementation of Mobile-URSONet: a mobile version of the popular URSONet that achieved the 3rd place on the European Space Agency's Pose Estimation Challenge. We propose Mobile-URSONet: a spacecraft pose estimation convolutional neural network with 178 times fewer parameters while degrading accuracy by no more than four times compared to URSONet.

For more details check our Arxiv preprint. This work was presented at IEEE ISCAS 2022, a peer-reviewed conference and available on IEEE Xplore

@INPROCEEDINGS{9937721,
  author={Posso, Julien and Bois, Guy and Savaria, Yvon},
  booktitle={2022 IEEE International Symposium on Circuits and Systems (ISCAS)}, 
  title={Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation}, 
  year={2022},
  volume={},
  number={},
  pages={794-798},
  doi={10.1109/ISCAS48785.2022.9937721}}

Installation

Data and directory structure

Mobile-URSONet is ready to use the SPEED dataset released by Stanford SLAB and ESA. Just download the dataset and follow the directory structure (or modify the config.py DATASET_PATH):

    ├── datasets            # Datasets folder
    │   ├── speed           # Speed dataset
    │   │   ├── images      # Speed images folder
    │   │   └──...          # JSON files, license, etc...
    │   └── others          # Potentially other Spacecraft Pose Estimation datasets
    │
    ├── mobile_ursonet      # Project root directory (Git)
    │   ├── display         # matplotlib figures saved duning training/evaluation
    │   ├── models          # weights of our models used in the article
    │   ├── optuna_tuning   # results of hyperparameter tuning with optuna
    │   ├── src             # python source files
    │   ├── submissions     # CSV files for submission on ESA website
    │   └── ...             # License, readme, etc...

Remark: resizing images to 960x600 pixels helps reduce the latency when images are loaded into RAM. See copy_speed_dataset_resize function in data.py

Dependencies

This code was developed using Python 3.8.12. Python packages are listed in requirements.txt. To install the necessary python packages with conda, execute:

conda install --file requirements.txt -c pytorch -c conda-forge

Training and evaluation

The training and evaluation of Mobile-URSONet is controlled by the config.py file. To execute the code, just modify the config.py file and run from the src directory: python main.py

Example: to reproduce the results of our 16 bins model, change the default value of `config.py to the following:

Variable	Value
TRAINING	False
MODEL_PATH	"../models/16bins_model.pt"
N_ORI_BINS_PER_DIM	16

See config.pyfor more details.