CloudNavi

Abstract

The rapid development of mobile computing has prompted indoor navigation to be one of the most attractive and promising applications. Conventional designs of indoor navigation systems depend on either infrastructures or indoor floor maps. This article presents CloudNavi, a ubiquitous indoor navigation solution, which relies on the point clouds acquired by the 3D camera embedded in a mobile device. Particularly, CloudNavi first efficiently infers the walking trace of each user from captured point clouds and inertial data. Many shared walking traces and associated point clouds are combined to generate the point cloud traces, which are then used to generate a 3D path-map. Accordingly, CloudNavi can accurately estimate the location of a user by fusing point clouds and inertial data using a particle filter algorithm and then guiding the user to its destination from its current location. Extensive experiments are conducted on office building and shopping mall datasets. Experimental results indicate that CloudNavi exhibits outstanding navigation performance in both office buildings and shopping malls and obtains around 34% improvement compared with the state-of-the-art method.