Image Set Querying Based Localization

TL;DR

This paper introduces an image-set querying based localization method that improves localization accuracy by using multiple auxiliary images and a local 3D model, especially when single image methods fail.

Contribution

The paper presents a novel approach that constructs a local 3D model from multiple images and estimates pose through nonlinear optimization, enhancing localization robustness.

Findings

Effective in scenarios with large view variations

Outperforms single image localization methods

Feasible with real-world experiments

Abstract

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.