Part 2: Understanding the Particle Filter

This video presents a high-level understanding of the particle filter and shows how it can be used in Monte Carlo localization to determine the pose of a mobile robot inside a building.  

We’ll cover why the particle filter is better suited for this type of problem than the traditional Kalman filter because of its ability to handle non-Gaussian probability distributions.

Additional Resources:

- More details on dead reckoning, MATLAB Tech Talk video: https://bit.ly/37T9BRT

- Understanding the Kalman Filter, MATLAB Tech Talk Series: https://bit.ly/314rLia

- Another good description of the particle filter: https://youtu.be/aUkBa1zMKv4

- Download ebook: Sensor Fusion and Tracking for Autonomous Systems: An Overview - https://bit.ly/2YZxvXA

- Download white paper: Sensor Fusion and Tracking for Autonomous Systems - https://bit.ly/2YZxvXA

- A Tutorial on Particle Filtering and Smoothing (includes AMCL). Paper by Doucet and Johansen: https://www.stats.ox.ac.uk/~doucet/do..