Watch the first video in this series here: https://youtu.be/Fw8JQ5Q-ZwU
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/doucet_johansen_tutorialPF2011.pdf
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