Crowd-based personal radars for indoor mapping using UWB measurements

The concept of personal radar has recently emerged as an interesting solution for future simultaneous localization and mapping (SLAM) applications. In this paper we evaluate the performance of an ultrawide-band (UWB) radar system for indoor environments mapping by exploiting a grid-based Bayesian approach able to combine all the measurements collected by radars adopting non-pencil beam antennas. In the proposed approach, the crowd will be involved by freely exploring the space, sending environmental partial views of it to cloud servers, where a complete map will be formed. Results show how the mapping accuracy can be improved thanks to the information collected from the crowd and considering different receivers schemes.