Abstract--Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Carlo Localization or Particle Filters method and send it to a goal state. ROS, Gazebo and RViz were used as the tools of the trade to simulate the environment and programming two robots for performing localization. The benchmark robot's URDF was given The benchmark robot was called UdacityBot the robot's current position and orientation. So, it is very and the 2nd robot was called SagarBot. The world-map is obvious that without this knowledge, the robot won't be called'Jackal-Race' and was created by Clearpath Robotics. There are 3 different types of localization problems.