RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Conditions

Jialu Wang; Muhamad Risqi U. Saputra; Chris Xiaoxuan Lu; Niki Trigoni; Andrew Markham

IROS 2023poster3 citations

RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Conditions

Jialu Wang, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham

Abstract

Camera localization is a fundamental problem for many applications in computer vision, robotics, and autonomy. Despite recent deep learning-based approaches, the lack of robustness in challenging conditions persists due to changes in appearance caused by texture-less planes, repeating structures, reflective surfaces, motion blur, and illumination changes. Data augmentation is an attractive solution, but standard image perturbation methods fail to improve localization robustness. To address this, we propose RADA, which concentrates on perturbing the most vulnerable pixels to generate relatively less image perturbations that perplex the network. Our method outperforms previous augmentation techniques, achieving up to twice the accuracy of state-of-the-art models even under ‘unseen’ challenging weather conditions. Videos of our results can be found at https://youtu.be/niOv7-fJeCA. The source code for RADA is publicly available at https://github.com/jialuwang123321/RADA.

BibTeX

@inproceedings{iros2023_radarobustadvers,
  title = {RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Conditions},
  author = {Jialu Wang and Muhamad Risqi U. Saputra and Chris Xiaoxuan Lu and Niki Trigoni and Andrew Markham},
  booktitle = {IROS 2023},
  year = {2023}
}