An Evolutionary Algorithm for Simultaneous Localization And Mapping (SLAM) With A New Fitness Function

In the robotic world, SLAM (Simultaneous Localization And Mapping) is a well-known and difficult problem. For solving this problem many solutions have been presented that are generally based on two methods (tools): EKF (Extended Kalman Filter) and Particle Filter. Each of these methods has some drawbacks, so researchers are looking for other ways for solve these problems. One of the major approaches to solve the SLAM problem is the approach based on evolutionary algorithm, and the algorithm proposed in this study is in the same category. Our final algorithm is hybrid Particle Filter and genetic algorithm for solving the SLAM problem but since one of the most important steps in genetic algorithm and our hybrid solution is fitness function, we want to introduce this step of our algorithm and show some of the results in a simulated environment.