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

TL;DR

RADA is a novel data augmentation method that focuses on perturbing geometrically informative image regions to improve camera localization robustness under challenging weather conditions, outperforming previous techniques.

Contribution

It introduces RADA, a targeted augmentation approach that enhances robustness to domain shifts in camera localization by perturbing key image regions.

Findings

RADA achieves up to two times higher accuracy than state-of-the-art models.

It significantly improves robustness under unseen challenging weather conditions.

Outperforms previous augmentation methods in robustness and accuracy.

Abstract

Camera localization is a fundamental and crucial problem for many robotic applications. In recent years, using deep-learning for camera-based localization has become a popular research direction. However, they lack robustness to large domain shifts, which can be caused by seasonal or illumination changes between training and testing data sets. Data augmentation is an attractive approach to tackle this problem, as it does not require additional data to be provided. However, existing augmentation methods blindly perturb all pixels and therefore cannot achieve satisfactory performance. To overcome this issue, we proposed RADA, a system whose aim is to concentrate on perturbing the geometrically informative parts of the image. As a result, it learns to generate minimal image perturbations that are still capable of perplexing the network. We show that when these examples are utilized as augmentation, it greatly improves robustness. We show that our method outperforms previous augmentation techniques and achieves up to two times higher accuracy than the SOTA localization models (e.g., AtLoc and MapNet) when tested on `unseen' challenging weather conditions.