CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

Abstract

In this paper, a Cooperative Multi-technology Simultaneous Localization and Signal Mapping (CM-SLASM) technique is proposed to improve the signal map accuracy and to build on-thefly a signal map to be used also in indoor environments where conditions can change over time. Moreover, the CM-SLASM combines WiFi, Ultra WideBand (UWB) and LIght Detection And Ranging (LIDAR) signals to improve positioning estimation by sharing information and cooperation among vehicles through Vehicle-To-Vehicle (V2V) communication links. In particular, LIDAR-based distance between vehicles is shared among neighbor vehicles to improve the vehicle positioning estimated by an Extended Kalman Filter (EKF) where WiFi fingerprinting is combined with UWB multilateration. The overall solution where EKF estimation allows to build more precise signal MAP is validated by simulation in a defined indoor scenario where vehicles equipped with different percentages of LIDAR, and different quantities of UWB and WiFi emitters have been considered. The proposed strategy has been validated through an extensive simulation campaign in various scenarios of interest and through a real-world experiment conducted in a laboratory test environment.