Exploration with FastSLAM technique

In this work we investigate the exploration of an environment with multiple vehicles using a strategy based on occupancy grids and a technique of simultaneous localization and mapping (SLAM). The exploration strategy uses concepts of costs and utility from frontier cells. Besides, the used SLAM method is based on a FastSLAM algorithm with landmarks extracted from visual sensors and a common map of characteristics. Both activities are coordinated by a central agent. The results obtained by simulation show that when two vehicles can communicate with a central agent building a common map of characteristics, the exploration task becomes more efficient than that performed with only one vehicle. This occurs because the exploration time is reduced while the accuracy of vehicle position and orientation is maintained or even improved with the same number of particles. In this paper, we present new results in the data association of FastSLAM algorithm.