Short Description
A demonstration of how a particle filter works in an easy-to-understand example. An imaginary robot moves and localizes itself in a simple 2D grid world. The idea is to help students understand the concept of particle filters with a well-structured, elaborately documented, high-quality Python example implementation. The GitHub repository guides through the example with explanations and visualizations. Students can use the example as a reference architecture for how to build a particle filter. They can play around with the provided example implementation, for example, by implementing their own movement model and measurement model. Thus, they can understand the strengths and limitations a particle filter has in combination with certain models. A Python API is provided: To implement your own measure or movement model, users only need to implement a child class of the provided Python interface "MeasurementModel" or "MovementModel".
More
For more information, see the GitHub repository: https://github.com/mabdn/bayesian-localization
Built With
- matplotlib
- python
