In the field of robotics and artificial intelligence, the primary task is to determine the mobile robot’s location, known as ‘Localization.’ Several methods can be used for mobile robot localization, such as the Kalman filter, Grid-based Markov Localization, and the Monte Carlo Localization (Particle filter). The Monte Carlo method or Particle filtering is currently the most popular localization method due to its optimal trade-off between accuracy and robustness. This method involves describing a probabilistic distribution through a sampling process. The map is sampled based on known prior probabilities, and each particle is assigned a weight representing its probability. Through iterations, the estimate pose of the robot accurately converges.