Abstract: Resampling process often causes the"sample impoverishment"problem 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 named"Genetic FastSLAM 2.0"is 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 named"car park dataset"to 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.