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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:
- Understanding the Kalman Filter, MATLAB Tech Talk Series:
- Another good description of the particle filter:
- Download ebook: Sensor Fusion and Tracking for Autonomous Systems: An Overview -
- Download white paper: Sensor Fusion and Tracking for Autonomous Systems -
- A Tutorial on Particle Filtering and Smoothing (includes AMCL). Paper by Doucet and Johansen:

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