RobustLoc

(AAAI 2023) RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments

💥💥

Requirements

Platform

CUDA>=11.0
python>=3.6

Pytorch installation (We have tested with Pytorch>=1.10 as well as newly released Pytorch2.0):

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Other dependencies:

colour_demosaicing
matplotlib
numpy
opencv_python
Pillow
scipy
tqdm
transforms3d
torchdiffeq

Dataset

We currently provide the Oxford RobotCar dataset that has been pre-processed.

https://drive.google.com/file/d/1xewI1Cfq7a-zQfk2oGoJW6zJ8ZhMu_mK/view?usp=share_link

[2023-06-13] The 4Seasons-related datasets&code have been uploaded at: https://drive.google.com/file/d/1H2ujRAd1v3reg31zDHoM1yBI0IUi1Ovz/view?usp=sharing

[2023-05-09] The 4Seasons-related datasets&code are in preparation. Kindly please tuned.
Train and test

Check in tools/options.py and set your own --data_dir as where you store the Oxford RobotCar dataset.
```
python train.py
python eval.py
```
Code reference

https://github.com/BingCS/AtLoc

https://github.com/psh01087/Vid-ODE
LiDAR-based Pose Regression

Feel free to check out our CVPR'2023 work, which uses LiDAR point clouds for pose regression

https://github.com/sijieaaa/HypLiLoc

https://arxiv.org/abs/2304.00932

Citation

@inproceedings{wang2023robustloc,
  title={RobustLoc: Robust camera pose regression in challenging driving environments},
  author={Wang, Sijie and Kang, Qiyu and She, Rui and Tay, Wee Peng and Hartmannsgruber, Andreas and Navarro, Diego Navarro},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={5},
  pages={6209--6216},
  year={2023}
}

sijieaaa/RobustLoc

RobustLoc