## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) [1] stands as a cornerstone technology in the fields of autonomous navigation and robotics, providing machines with the ability to explore and map new territories without prior knowledge of the environment. This technology is not only crucial for the autonomous operation of robots, but also forms the basis for higher-level functionalities such as environmental perception, strategic decision-making, and autonomous pathfinding. By integrating sensor data from sources like LiDAR, optical cameras, and inertial sensors, SLAM facilitates the concurrent creation of environmental maps and the precise positioning of the robot in real-time. This dual capability is vital for robots to function autonomously, especially in environments that are subject to constant change [2]. As the frontier of technology advances, SLAM has become a fundamental element of smart systems, driving innovation and progress in the automation sector [3]. The ongoing research and refinement of SLAM techniques are of significant importance for the advancement of intelligent systems and their applications in the future. The effective implementation of SLAM in unknown and dynamically changing environments, however, presents substantial technical hurdles that the field must address to ensure the reliability and efficiency of robotic systems. FastSLAM [4] is a prominent approach to the SLAM challenge, renowned for its efficiency in navigating the complexities of non-linear systems and handling noise that does not conform to the traditional Gaussian model. This algorithm employs a synergistic approach by integrating the robustness of particle filtering with the precision of the Extended Kalman Filter (EKF) [5]. While Gaussian noise is predictable and follows a standard bell-shaped distribution, real-world scenarios often present noise patterns that are non-Gaussian, complicating the task for SLAM algorithms. FastSLAM addresses this by segmenting the SLAM process into two principal tasks: the robot's pose estimation, which is managed by particle filtering to accommodate non-linear dynamics, and the landmark estimation, which is processed by the EKF to handle each landmark individually. This bifurcation significantly alleviates computational burden and supports the algorithm's scalability in expansive environments. Despite these strengths, FastSLAM faces notable difficulties when operating in settings with extensive environments or under conditions with substantial noise interference [6]. The phenomenon of particle degeneration can occur, where the diversity among particles is compromised, potentially impeding comprehensive exploration of the state space. In the course of multiple iterations, the algorithm may encounter a situation where only a few particles with substantial weights come to dominate the posterior distribution, overshadowing the majority. Such a scenario impairs the algorithm's capacity to accurately capture the true posterior probability and leads to a suboptimal use of computational resources. Building upon the challenges faced by FastSLAM in maintaining robustness amidst particle degeneration, particularly in complex and dynamic settings, the introduction of Multi-Objective Particle Swarm Optimization (MO-PSO) [7] presents a novel perspective. MO-PSO is an evolutionary computation technique inspired by the social behavior of bird flocking and fish schooling, which is adept at handling multi-objective optimization problems. Unlike traditional Particle Swarm Optimization (PSO) [8], which focuses on a single objective, MO-PSO is designed to optimize multiple conflicting objectives simultaneously, providing a more comprehensive solution landscape. The algorithm guides a swarm of particles through the search space, allowing them to adapt their positions and velocities based on both personal and collective experiences, thereby converging towards a set of optimal solutions rather than a single point.