The Particle Filter is one of my FAVOURITE algorithms. It's so simple to understand and to implement, yet the performance is quite robust! The central idea behind the particle filter is to brute force your way to the solution. Start with a bunch of particles that represent where you think you are right now. Then, for the prediction step, propagate each particle through the motion model. Following in the correction step, if a particle is unlikely to produce the measurement we obtain, just get rid of it! Repeat. See? It just took a few sentences to explain. Hope you enjoy!
Importance Sampling Tutorial: • Importance Sampling: A...
Chapters:
0:00 Video Introduction
0:24 Model Setup
1:08 Importance Sampling
1:37 Theoretical Derivation
3:34 Practical Implementation - Madow Systematic Sampling
4:26 Practical Implementation - Importance Weight Calculation
5:00 Tips and Tricks
5:46 Example!