  Accurate localization and 3D maps are increasingly needed for various
artificial intelligence based IoT applications such as augmented reality,
intelligent transportation, crowd monitoring, robotics, etc. This article
proposes a novel semantic 3D map change detection and update based on a
smartphone visual positioning system (VPS) for the outdoor and indoor
environments. The proposed method presents an alternate solution to SLAM for
map update in terms of efficiency, cost, availability, and map reuse. Building
on existing 3D maps of recent years, a system is designed to use artificial
intelligence to identify high-level semantics in images for positioning and map
change detection. Then, a virtual LIDAR that estimates the depth of objects in
the 3D map is used to generate a compact point cloud to update changes in the
scene. We present an excellent performance of localization with respect to
other state-of-the-art smartphone positioning solutions to accurately update
semantic 3D maps. It is shown that the proposed solution can position users
within 1.9m, and update objects with an average error of 2.1m.
