Compressive sensing approach based mapping and localization for mobile robot in an indoor wireless sensor network

Reliable navigation in the Wireless Sensor Networks (WSNs) always require mobile robot do localization and environment map building processes, which depends heavily on estimating the position of the features within the entire surroundings, that means as a sensor receiving platform, the robot needs to detect and process information transmitted from sensors as much as possible, in order to perform tasks. However, some large and complex deployed wireless sensor network environments, in which sensor information are relatively sparse compared with the number of sensor sources, usually make the robot hard to receive enough crucial information. To make robot know its position and construct the environment map with minimal sensing information. We propose a novel navigation algorithm based on RF wireless sensor networks to simultaneous localization and mapping (SLAM) approach, thus, a new framework that allows a team of robots to build a map of the parameter of interest with a small number of measurements is presented. By using the recent results in the area of compressive sensing, we show how the robots can build a map with limited number of sensing measurements. The proposed algorithm is conceptually simple and easy to implement. Simulation and experimental results show that good result can be achieved using the proposed method.