Image Set Querying Based Localization
This addresses localization failures in computer vision applications, but it is incremental as it builds on existing single-image methods.
The paper tackles the problem of image-based localization failing under large variations by proposing an image-set querying approach, where auxiliary images are used to build a local 3D model and estimate pose, showing effectiveness in experiments.
Conventional single image based localization methods usually fail to localize a querying image when there exist large variations between the querying image and the pre-built scene. To address this, we propose an image-set querying based localization approach. When the localization by a single image fails to work, the system will ask the user to capture more auxiliary images. First, a local 3D model is established for the querying image set. Then, the pose of the querying image set is estimated by solving a nonlinear optimization problem, which aims to match the local 3D model against the pre-built scene. Experiments have shown the effectiveness and feasibility of the proposed approach.