The goal of Temporal Camera Relocalization is to efficiently and effectively estimate the 6-DoF camera posew.r.tworld coordinate system. It is one of the fundamental problems in Augmented Reality and Autonomous Driving. However, most of the current approaches focus on one-shot image localization with an emphasis on a single RGB image for camera pose estimation, and the accuracy of TCR methods falls behind the SoTA one-shot methods even taking the time dependency into account.
This thesis proposes a novel Temporal Camera Relocalization pipeline, which consists of three parts: global keyframe localization, local odometry, and fusion algorithms. The global localization has a hierarchical structure and can output image poses with high accuracy, the local tracking is provided by the latest visual-inertial odometry platform. Two fusion algorithms, global constraints and particle filter based method, are proposed in this thesis to utilize both global and local information for temporal camera relocalization. Experimental results show that both methods have promising performances with a mean error of less than0.48m/0.68◦in a space of30*20*2m3. The global constraints method achieves the best result with a mean errorof0.22m/0.2◦, the particle filter based method is robust to global pose estimation and has the ability to maintain the performance when the accuracy of global localization is significantly dropped.
