Filter_Based_Localization HW

The starter code is written in Python and depends on NumPy and Matplotlib. This README gives a brief overview of each file.

• localization.py -- This is your main entry point for running experiments.
• soccer_field.py -- This implements the dynamics and observation functions, as well as the noise models for both.
• utils.py -- This contains assorted plotting functions, as well as a useful function for normalizing angles.
• policies.py -- This contains a simple policy, which you can safely ignore.
• ekf.py -- Add your extended Kalman filter implementation here!
• pf.py -- Add your particle filter implementation here!

Command-Line Interface

To visualize the robot in the soccer field environment, run

$ python localization.py --plot none

The blue line traces out the robot's position, which is a result of noisy actions. The green line traces the robot's position assuming that actions weren't noisy.

After you implement a filter, the filter's estimate of the robot's position will be drawn in red.

$ python localization.py --plot ekf
$ python localization.py --plot pf

You can scale the noise factors for the data generation process or the filters with the --data-factor and --filter-factor flags. To see other command-line flags available to you, run

$ python localization.py -h

more please refer to the website