Relative Pose Regression

Relative pose regression (RPR) aims to estimate the relative position and orientation of a camera between two images or relative to a known map, a crucial task in various applications like robotics and augmented reality. Current research focuses on improving the accuracy and generalization of RPR methods, employing architectures such as graph neural networks and recurrent convolutional networks, often incorporating techniques like image matching and multimodal fusion with inertial data to enhance robustness. These advancements address challenges like limited training data and scene variations, leading to more reliable and efficient camera localization in diverse environments.