Localization Module

The localization module takes information from the vision component and attempts to accurately locate the positions of the ball and the two robots on the field. It does this using a Monte Carlo Localization (MCL) model called a particle filter. A particle filter is a probabilistic model which maintains a collection of guesses as to the location of the robots in the form of x and y location on the field as well as the bearing of the robot from -PI/2 to PI/2. Data comes into the particle filter from the vision component as a set of objects that are recognized by the robot, and where they are in relation to the robot. Using this information and the known locations of these objects such as the goal, the particle filter updates the probability of each sample location, and resample around the more likely samples.
The specific approach implemented in this project is a form of MCL known as Adaptive MCL (AMCL). This means that the number of particles in each model can be changed on the fly to account for more uncertain information. If the standard deviation of the particles grows too large, more particles are evenly distributed over the field as to try to localize over a broader area. In order to localize the ball, there is one particle filter running on each robot, and these two models share data between them. The location of the ball is determined to be the most likely position given all of the data from both robots. However, if one robot cannot see the ball, then it learns the location from the other robot.