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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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
Original languageEnglish
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages3335-3342
Number of pages8
ISBN (Electronic)978-1-6654-9190-7
ISBN (Print)978-1-6654-9191-4
DOIs
Publication statusPublished - 13 Dec 2023
Event2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 - Detroit, United States
Duration: 1 Oct 20235 Oct 2023
https://ieee-iros.org/

Publication series

NameProceedings of the International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Abbreviated titleIROS 2023
Country/TerritoryUnited States
CityDetroit
Period1/10/235/10/23
Internet address

Fingerprint

Dive into the research topics of 'RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Conditions'. Together they form a unique fingerprint.

Cite this