Improved RO-SLAM using activity classification for automated V2X infrastructure mapping

In recent years, wireless sensor networks became popular for a wide range of mainstream applications. Closely related with this evolution, a problem for consumer market use emerged: How to initialize and setup the infrastructure automatically. This paper presents an approach to solve this problem. We present a novel approach how to build infrastructure maps only with anchor-mobile range measurements. The approach uses a baseline SLAM implementation in form of incremental posterior mapping. We adapt the approach by representing mobile posterior as well as anchor maps with probability grids similar to Markov Localization due to addressing the complex Range Only Simultaneous Localization and Mapping (RO-SLAM) problem. In urban areas mobiles are employed e.g. by pedestrians or bikes which feature a specific kinematic locomotion activity. Hence, we pair RO-SLAM with a SVM-based activity classifier in order to raise anchor mapping accuracy. Simulation results discuss algorithm convergence and demonstrate the accuracy improvement in the presence of activity information.