CLoc: Confident Initial Estimation of Long-Term Visual Localization Using a Few Sequential Images in Large-Scale Spaces

Estimation of six degree-of-freedom (DoF) camera pose with images is a fundamental task for various applications, such as autonomous driving and augmented reality. The challenges in visual localization stem primarily from the scale of spaces and the changes that can happen during the long-term intervals between when the database and query images are captured. Although most sequence-based visual localization (SVL) methods rely on the outputs of one-shot visual localization (OVL) for the initial estimation of the camera pose, OVL may yield incorrect poses because of these limitations. In this study, we aim to achieve a confident initial estimation using a few sequential images. Accordingly, we propose a pipeline called CLoc that narrows down possible solution spaces with hypotheses generated by associating pose candidates of keyframes and their relative poses to determine the most probable pose of the latest keyframe. Moreover, we create publicly available visual localization datasets, including three building-scale indoor spaces. The datasets provide long-term challenges and sequential query images simultaneously, enabling the evaluation of the OVL and SVL methods in large-scale spaces. We evaluate CLoc using our datasets and a city-scale outdoor dataset and compare it with two state-of-the-art OVL methods and three SVL methods. By properly leveraging sequential data, CLoc can outperform other methods and achieves 100% accuracy in the [2 m, 10°] evaluation thresholds for all datasets.