Local Supports Global: Deep Camera Relocalization with Sequence Enhancement

Fei Xue, Hongbin Zha

Abstract:

We propose to leverage the local information in image
sequences to support global camera relocalization. In contrast to previous methods that regress global poses from single images, we exploit the spatial-temporal consistency in
sequential images to alleviate uncertainty due to visual ambiguities by incorporating a visual odometry (VO) component. Specifically, we introduce two effective steps called
content-augmented pose estimation and motion-based refinement. The content-augmentation step focuses on alleviating the uncertainty of pose estimation by augmenting the
observation based on the co-visibility in local maps built
by the VO stream. Besides, the motion-based refinement is
formulated as a pose graph, where the camera poses are
further optimized by adopting relative poses provided by
the VO component as additional motion constraints. Thus,
the global consistency can be guaranteed. Experiments on
the public indoor 7-Scenes and outdoor Oxford RobotCar
benchmark datasets demonstrate that benefited from local
information inherent in the sequence, our approach outperforms state-of-the-art methods, especially in some challenging cases, e.g., insufficient texture, highly repetitive textures,
similar appearances, and over-exposure.