RobustLoc: robust camera pose regression in challenging driving environments

• Main Authors: Wang, Sijie, Kang, Qiyu, She, Rui, Tay, Wee Peng, Hartmannsgruber, Andreas, Navarro, Diego Navarro
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Visual Relocalization; Autonomous Driving
• Online Access: https://hdl.handle.net/10356/165272; https://aaai.org/aaai-publications/aaai-conference-proceedings/
• Institution: Nanyang Technological University

Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. Our model uses a convolutional neural network to extract feature maps from multi-view images, a robust neural differential equation diffusion block module to diffuse information interactively, and a branched pose decoder with multi-layer training to estimate the vehicle poses. Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments.