Autonomous Navigation, Part 2: Understanding the Particle Filter

Watch the first video in this series here: 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)