This report describes the Monte Carlo approach to the localization of a robot or autonomous system. Localization in robot or autonomous systems is the problem of position determination using sensor data. The Monte Carlo method is estimated by making statistical inferences. However, the noisy data from the sensors can change the instantaneous state of the robot or an autonomous system. To overcome this problem, the Monte Carlo algorithm family uses the state tree of the Particle Filter. Monte Carlo algorithm predicts the posterior proximity of a robot using a set of weighted sampling methods. Experimental results show the effectiveness of the proposed algorithm.