Monte Carlo Particle Filter for Localization
Particle Filter Algorithm is a nonparametric implementation of the Bayes Filter to approximate state, for example of a robot moving in a maze.
The idea is to represent the posterior belief by a finite number of random variables (particles). The algorithm is repeatedly resampling those particles based on likelihood derived from a measurement model.
This project is a examination project for SS18 Monte Carlo Methods in Artificial Intelligence and Machine Learning course taught by Prof. Dr. Manfred Opper and Theo Galy-Fajou, at TU Berlin.
pip install -r requirements.txt
> python particle-filter.py Options - scene : [scene-1, scene-2, scene-1-kidnapping, scene-2-kidnapping, scene-8.12] - no_particles : number of particles, for example 100. - total_frames : total time step to run the simulation, it's useful for debugging. - show_particles : a boolean option whether to show the particles. - no_random_particles: number of random particles introduced to the system, required for kidnapping scenes. - save : a boolean option whether to see the simulartor live or save the result as a video. - frame_interval : time interval for each frame, default is 50s.
python particle-filter.py --scene scene-1 --no-particles 100 --save
Anders Dahl Hjort, Luis Dreisbach, and Pattarawat Chormai