## 2 Related Works
### Related Work of Integrating FastSLAM with Evolutionary Algorithm  
In the domain of SLAM, the FastSLAM algorithm adeptly addresses the challenges of nonlinearity and non-Gaussian noise by synergistically merging the strengths of PF with the EKF. Despite its efficacy, FastSLAM encounters significant difficulties when tackling the degradation issues of particle filtering under conditions of large-scale environments and high noise levels. To surmount these obstacles, researchers have endeavored to integrate the PSO algorithm with FastSLAM, aiming to enhance the robustness and accuracy. This integration leverages the global search capabilities of PSO to refine the particle distribution, thereby mitigating particle degradation and augmenting the overall performance of the SLAM system.  
[PERSON] et al. [18] present an effective approach for the global localization of mobile robots using Differential Evolution and Particle Swarm Optimization. By integrating evolutionary algorithms with probabilistic motion and observation models, the proposed methods demonstrate superior performance in terms of convergence rate, accuracy, and computational efficiency, offering a robust solution for initial pose determination and pose tracking in mobile robotics. [PERSON] et al. [19] introduce the Evolutionary Localization Filter (ELF), which effectively addresses the challenges of global localization without relying on initial pose information by leveraging evolutionary computation concepts and focusing computational resources on the most promising areas. [PERSON] et al. [20] present an improved particle filter for mobile robot global localization based on particle swarm optimization. The proposed method, named particle swarm optimization filter, achieves robust and accurate positioning results in indoor environments with fewer particles compared to benchmark methods. This advancement could be integrated into a wide range of mobile robot systems to reduce computational costs and improve navigation efficiency. [PERSON] et al. [21] introduce an enhanced particle filter SLAM algorithm that employs PSO to bolster the accuracy and robustness of localization within similar environments. The efficacy is substantiated through comparative experiments with the Gmapping algorithm, demonstrating superior performance in terms of particle count, localization precision, and the topological correctness of robotic navigation. [PERSON] et al. [22] introduce a novel global localization filter that integrates Differential Evolution with Markov Chain Monte Carlo (MCMC) methods, effectively enhancing the accuracy of global localization for mobile robots in both 2D and 3D environments. Through testing in simulated and real-world scenarios, the approach has demonstrated significant advantages in reducing the requirement for particles and improving localization accuracy.  
The collective research endeavors in the realm of SLAM have demonstrated that the amalgamation of evolutionary algorithms with the FastSLAM framework can significantly enhance the performance of SLAM algorithms, particularly when navigating dynamic environments and large-scale scenarios. These hybrid approaches have not only bolstered the precision of localization but also augmented the capacity to adapt to environmental fluctuations, thereby paving new avenues for the advancement of SLAM technologies. Despite the notable improvements in SLAM performance, challenges persist, such as the computational complexity of the algorithms and their adaptability to specific environmental conditions.