Compressive cooperative obstacle mapping in mobile networks

In this paper we consider a mobile cooperative network that is tasked with building an obstacle map in an environment. We propose a framework that allows the robots to build the obstacle map non-invasively and with a small number of wireless channel measurements. By extending our previous work on sparse obstacle mapping, we show how the nodes can exploit the sparse representation of the map in order to build it with minimal sensing. The proposed work allows the robots to efficiently map an area before entering it. We propose two approaches based on random and coordinated wireless measurements. Our simulation and experimental results show the superior performance of the proposed framework.