https://github.com/emmanuelmess/robotica-movil-tp4

https://github.com/emmanuelmess/robotica-movil-tp4

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• Host: GitHub
• URL: https://github.com/emmanuelmess/robotica-movil-tp4
• Owner: EmmanuelMess
• Created: 2023-10-22T16:54:59.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2023-12-01T20:20:43.000Z (12 months ago)
• Last Synced: 2023-12-01T21:28:17.149Z (12 months ago)
• Language: Python
• Size: 54.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# filters

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. Add your Jacobian implementations here!

- `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

```bash

$ 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.

```bash

$ 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

```bash

$ python localization.py -h

```