DeepLocBIM: Learning Indoor Area Localization Guided by Digital Building Models

Abstract

Fingerprinting-based indoor localization is a cost-effective approach to provide coarse-grained indoor location information for pedestrian mass-market applications without the requirement of installing additional positioning infrastructure. While most solutions aim at pinpointing the exact location of a user, estimating a zone/area is a promising approach to achieve a more reliable prediction. Area localization predominantly utilizes a predetermined building model segmentation to obtain zone/area labels for collected fingerprints. We propose a novel approach to multifloor indoor area localization by directly predicting polygon zones that contain the position of the user. Our model learns to construct the zones from the wall segments and thus predicted areas have a high conformity to the underlying building model (semantic expressiveness). On a self-collected as well as on a public fingerprinting data set, we compare our model with two reference approaches. We demonstrate that the utilized surface areas of the polygons are on average up to 50% smaller than those of the reference models and provide a high semantic expressiveness without requiring manual floor plan segmentation.