  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.
