Visual Localization Using Sparse Semantic 3D Map
This addresses robust camera localization for computer vision and robotics applications, but appears incremental as it builds on existing methods with semantic enhancements.
The paper tackles visual localization under varying conditions by combining structure-based and image-based methods with semantic information, achieving significant improvement on a challenging benchmark dataset.
Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications. Under these conditions, most traditional methods would fail to locate the camera. In this paper we present a visual localization algorithm that combines structure-based method and image-based method with semantic information. Given semantic information about the query and database images, the retrieved images are scored according to the semantic consistency of the 3D model and the query image. Then the semantic matching score is used as weight for RANSAC's sampling and the pose is solved by a standard PnP solver. Experiments on the challenging long-term visual localization benchmark dataset demonstrate that our method has significant improvement compared with the state-of-the-arts.