InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor spaces. The method proceeds along three steps: (i) efficient retrieval of candidate poses that scales to large-scale environments, (ii) pose estimation using dense matching rather than sparse local features to deal with weakly textured indoor scenes, and (iii) pose verification by virtual view synthesis that is robust to significant changes in viewpoint, scene layout, and occlusion. Second, we release a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data. Code and data are publicly available.

1 Introduction

Autonomous navigation inside buildings is a key ability of robotic intelligent systems [27], [45]. Successful navigation requires both to localize a robot and to determine a path to its goal. One approach to solve the localization problem is to build a 3D map of the building and then use a camera to estimate the current position and orientation of the robot (Fig. 1). Imagine also the benefit of an intelligent indoor navigation system that helps you find your way, for example, at Chicago airport, Tokyo Metropolitan station or a convention center. Besides intelligent systems, the visual localization problem is also highly relevant for any type of Mixed Reality applications, including Augmented Reality [19], [52], [88].