## Particle Filter Localization Simulation

Let us assume the following: You want a robot that will fetch something from somewhere or should take something to someplace. In order for this mobile robot to know if it reached the goal, or to plan a path to the goal, it needs to know where it is.

This 'knowing where it is' is known as localization. One method used for localization is Particle Filters (Monte Carlo Localization) and this simulation implements particle filter localization.

The simulation consists of RED particles, a GREEN robot and BLUE landmarks (add landmarks by LEFT clicking on the map). By moving the robot around using the w,a,d keys you will see how the particles cluster around the robot, essentially showing where the robot is. (NOTE: If you do not add any landmarks the robot will not be able to localize.)

## Fitting a Linear Line to Data Points

A line of best fit is a line that is the best approximation of a given set of data. This line can be linear, exponential, logarithmic, polynomial, a power average or a moving average and will depend on the type of data you have and the purpose of the approximated line. Your dataset can even be in 3 dimensions.

I'm only going to look at 2D data (ordered pairs) and the Least Square Method to find a straight line ($y=mx+c$) that best fit the given data points.