A simple implementation of the MCL particle filter algorithm, as outlined by Thrun and Norvig in their online Intro to AI course.

This program is designed to be used under ROS (Robot Operating System). Specifically, use the UML_MCL package and the MCL tools helper kit. It is a simulation of a robot in 60m x 4m hallway, where the goal is to help the robot localize and find its way to a goal.

The robot has 5 range sensors at -90, -45, 0, 45, and 90 degrees, each with a max range of 5 meters. The sensors are imperfect, so there is error in the values they return. The code for generating error can be found in sensor.py.

The robot is able to spin and move forward. The motors are imperfect, so the robot does not always do exactly what it is told to do. The code for generating error can be found in motor.py.

The localization code is located in MCL.py. There are three parts to the code: particle filter, wall avoidance, and clustering. The robot will wander until it has localized with a high confidence, then drive to the goal (in between the two Xs, around (-8, -4)).