Reduced Search Space Algorithm for Simultaneous Localization and Mapping in Mobile Robots

In this paper, we propose a new algorithm for simultaneous localization and mapping in mobile robots which uses evolutionary algorithm and particle swarm optimization. The proposed method is based on both local and global heuristic search methods. In each step of robot movements, the local search is applied in the small search space of odometry errors to improve the map accuracy. A global search method is applied for loop closing. The proposed algorithm detects loops and closes them, detects and solves correspondence and avoids local extremums. With a proper representation of problem parameters in chromosome, the dimensionality of search space is reduced. The proposed algorithm utilizes occupancy grid and does not require land marks which are not available in most natural environments. A new fitness function is proposed that is computationally efficient and eliminates the need for complex statistical calculations as used in current approaches. Results of experiments on real datasets exhibit the superior performance of the proposed method compared to the current methods.