A Decentralized Architecture for Simultaneous Localization and Mapping

In this paper, a decentralized data fusion algorithm is presented for simultaneous position estimation of a land vehicle and building the map of the environment. Two independent loops, one incorporating inertial sensor and GPS data, and one fusing the laser data and the readings of the wheel and steering encoders, are considered. The information obtained from the sensors is first synchronized and then communicated to the other loop to enhance the quality of local loop estimates. The real data obtained from an experiment are used in implementing the algorithm and the information form of the Kalman filter is used as the main tool for the decentralized data fusion. It is shown that the algorithm leads to more accurate estimates as compared to the local loop estimates, and can perform properly even in the case of GPS masking.