ALIMC: Activity Landmark-Based Indoor Mapping via Crowdsourcing

Abstract

Indoor maps are integral to pedestrian navigation systems, an essential element of intelligent transportation systems (ITS). In this paper, we propose ALIMC, i.e., Activity Landmark-based Indoor Mapping system via Crowdsourcing. ALIMC can automatically construct indoor maps for anonymous buildings without any prior knowledge using crowdsourcing data collected by smartphones. ALIMC abstracts the indoor map using a link–node model in which the pathways are the links and the intersections of the pathways are the nodes, such as corners, elevators, and stairs. When passing through the nodes, pedestrians do the corresponding activities, which are detected by smartphones. After activity detection, ALIMC extracts the activity landmarks from the crowdsourcing data and clusters the activity landmarks into different clusters, each of which is treated as a node of the indoor map. ALIMC then estimates the relative distances between all the nodes and obtains a distance matrix. Based on the distance matrix, ALIMC generates a relative indoor map using the multidimensional scaling technique. Finally, ALIMC converts the relative indoor map into an absolute one based on several reference points. To evaluate ALIMC, we implement ALIMC in an office building. Experiment results show that the 80th percentile error of the mapping accuracy is about 0.8–1.5 m.