Compressive Cooperative Obstacle Mapping in Mobile Networks
In this paper, the authors consider a mobile cooperative network that is tasked with building an obstacle map in an environment. They 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 their previous work on sparse obstacle mapping, they show how the nodes can exploit the sparse representation of the map in order to build it with minimal sensing. The paper allows the robots to efficiently map an area before entering it. They propose two approaches based on random and coordinated wireless measurements. Their simulation and experimental results show the superior performance of the proposed framework.