## 5 Conclusions  
In this study, we have presented an innovative enhancement to the Simultaneous Localization and Mapping framework by integrating Particle Swarm Optimization with the FastSLAM algorithm. This integration was designed to address the critical issue of particle degradation, which commonly affects the accuracy and robustness of SLAM in complex and dynamic environments. Our approach leverages the global search capabilities of PSO to optimize the distribution of particles, thereby enhancing the performance of the FastSLAM algorithm. We introduced a novel fitness function tailored to the PSO algorithm, which was instrumental in guiding the particles toward regions of higher posterior probability. This strategic enhancement not only mitigated the degradation of particles but also improved the overall estimation accuracy and robustness of the SLAM solution across various operational scenarios. Through a series of meticulously designed experiments, we validated the effectiveness of our proposed method. The experimental results demonstrated that  
Figure 10: Analysis of the population parameters \(N\) in the large-scale scenario scene\(\_1\).  
our PSO-enhanced FastSLAM algorithm significantly outperformed the standard particle filtering approach, particularly in terms of localization accuracy and robustness against particle degeneration.  
The superiority of FastSLAM-PSO lies in its ability to maintain high accuracy and robustness in environments with high noise levels and large-scale spaces, which is a common challenge faced by traditional SLAM algorithms. Unlike methods that struggle with non-Gaussian noise, our PSO-enhanced FastSLAM algorithm uses the global search capability of PSO to effectively optimize particle distribution, leading to improved estimation accuracy and robustness. This advancement positions our method at the forefront of SLAM technology, offering a promising solution for real-world applications where traditional algorithms fall short. The insights gained from this research have highlighted the potential of swarm intelligence techniques in enhancing SLAM algorithms. The success of our approach suggests that by optimizing the particle distribution, we can achieve more precise and reliable SLAM performance, even in challenging environments characterized by high noise levels and large-scale spaces. This study has also inspired future work, which will focus on developing adaptive mechanisms that can dynamically adjust the particle count and other PSO parameters in real time based on the environmental conditions and the state of the algorithm.  
Conceptualization, L.T.; methodology, X.B.; software, W.Z.; formal analysis, W.Z.; investigation, X.B.; resources, H.Z. and X.M.; data curation, L.T.; writing--review and editing, X.B.; supervision, H.Z. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2022 YFB3305000.  
Not applicable.  
The data presented in this study are openly available in MDPI Research Data Policies\" at [[https://www.mdpi.com/ethics](https://www.mdpi.com/ethics)]([https://www.mdpi.com/ethics](https://www.mdpi.com/ethics)) (accessed on 1 September 2024).  
The authors declare no conflicts of interest.