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

Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System