A FastSLAM 2.0 Algorithm Based on Genetic Algorithm

Abstract

Resampling process often causes thesample impoverishmentproblem in FastSLAM 2.0.In order to improve the algorithm performance and to increase the estimation accuracy,FastSLAM 2.0 is combined with genetic algorithm,and a solution namedGenetic FastSLAM 2.0is presented for the SLAM problem.Based on the specialty of FastSLAM 2.0, an improved genetic algorithm is designed with attention to both the particle weight and the samples' diversity.Genetic FastSLAM 2.0 estimates the robot path with unscented particle filter (UPF),and the map with extended Kalman filter (EKF). Experiments are carded out with a benchmark dataset namedcar park datasetto evaluate performance of the genetic Fast- SLAM 2.0,and the results indicate that the genetic FastSLAM 2.0 performs well on both estimation accuracy and consistency, and the computational complexity satisfies the requirements from real-time applications.