## 3 Related Works of Enhancements to SLAM
### Other Improvements to FastSLAM  
This section primarily introduces improvements to FastSLAM, beginning with FastSLAM 2.0. [PERSON] et al. [17] presented an enhanced algorithm for SLAM, titled FastSLAM 2.0. This algorithm employs a sophisticated particle filtering technique, which considers recent sensor measurements in its proposal distribution. This modification leads to a more efficient utilization of particles, particularly in scenarios characterized by high motion noise relative to sensor noise. The researchers demonstrated the convergence of their algorithm for linear SLAM problems and reported a marked enhancement in accuracy over the original FastSLAM through real-world experiments. [PERSON] et al. [18] introduced an improved FastSLAM algorithm that amalgamates revised genetic resampling with the square root unscented particle filter (SR-UPF). This innovative approach tackles the traditional shortcomings, such as particle depletion and the linear approximation of non-linear functions. The integration of genetic resampling and SR-UPF enriches particle diversity and bolsters estimation precision. The algorithm's performance was validated through simulations and experiments, which revealed superior estimation accuracy and reliability compared to the conventional FastSLAM. [PERSON] et al. [19] advanced the FastSLAM system by implementing a distributed structure, resulting in a more robust system for autonomous robot navigation. Their system employs a distributed unscented particle filter and a linear observation model, effectively addressing the computational challenges and potential faults triggered by changing feature points. The algorithm's convergence was mathematically proven in the mean square sense, and simulations confirmed the algorithm's ability to reduce computational complexity while sustaining high estimation accuracy and system fault tolerance. These findings underscore the distributed potential for enhancing robotic navigation in complex environments. [PERSON] et al. [20] proposed a novel framework termed IFastSLAM, which enhances the performance of the conventional FastSLAM algorithm by integrating PSO. They introduced an adaptive genetic algorithm for resampling to increase particle diversity and employed fractional differential theory and chaotic optimization to refine the PSO approach. This innovation accelerated the algorithm's iteration and prevented premature convergence, leading to superior performance in simulations and field experiments. [PERSON] et al. [21] presented a strategy to augment the precision of self-positioning in FastSLAM by considering multiple candidate particles. This approach stored several candidates and utilized them to determine the next step's self-position estimate. By maintaining multiple particles, the proposed method achieved more robust self-position estimation and mapping compared to the traditional FastSLAM. The performance was evaluated through numerical simulations, demonstrating improved accuracy with an increase in candidate particles. [PERSON] et al. [22] developed an improved adaptive FastSLAM algorithm known as AFastSLAM, which incorporates a time-varying noise estimator. This algorithm utilized maximum likelihood estimation, expectation maximization criterion, and a one-step smoothing algorithm in importance sampling to enhance the FastSLAM method. The AFastSLAM algorithm was shown to provide significantly more accurate and consistent estimates for both robot and landmark positions across various simulations with different initial process and measurement noise values. The advancements in FastSLAM have been marked by significant strides, with researchers introducing various innovative approaches to enhance its performance. From the introduction of FastSLAM 2.0, which employs sophisticated particle filtering techniques, to the integration of genetic algorithms and fractional differential theory, each improvement aims to address specific challenges such as particle depletion and the linear approximation of non-linear functions.