  Simultaneous Localization and Mapping (SLAM) presents a formidable challenge
in robotics, involving the dynamic construction of a map while concurrently
determining the precise location of the robotic agent within an unfamiliar
environment. This intricate task is further compounded by the inherent
"chicken-and-egg" dilemma, where accurate mapping relies on a dependable
estimation of the robot's location, and vice versa. Moreover, the computational
intensity of SLAM adds an additional layer of complexity, making it a crucial
yet demanding topic in the field. In our research, we address the challenges of
SLAM by adopting the Particle Filter SLAM method. Our approach leverages
encoded data and fiber optic gyro (FOG) information to enable precise
estimation of vehicle motion, while lidar technology contributes to
environmental perception by providing detailed insights into surrounding
obstacles. The integration of these data streams culminates in the
establishment of a Particle Filter SLAM framework, representing a key endeavor
in this paper to effectively navigate and overcome the complexities associated
with simultaneous localization and mapping in robotic systems.
