Probabilistic Robotics

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# Topic to Chapter Mapping

  1. Localization

    • Chapter 1: Introduction

    • Chapter 2: Recursive State Estimation

    • Chapter 4: Nonparametric Filters, Sections 4.1-4.2

    • Chapter 5: Robot Motion (not listed in the canvas links, but some useful stuff in here)

    • Chapter 6: Robot Perception (not listed in the canvas link, but some useful stuff in here)

    • Chapter 7: Mobile Robot Localization, Sections 7.1-7.3

  2. Kalman Filters

    • Chapter 3: Gaussian Filters, Sections 3.1-3.2 (but checkout the sections on EKF and UKF if you want a deeper dive)
  3. Particle Filters

    • Chapter 4: Nonparametric Filters, Sections 4.3 (but checkout the whole chapter for a deeper dive)

    • Chapter 8: Mobile Robot Localization. Sections 8.3

  4. Search

    • Chapter 8: Mobile Robot Localization, Sections 8.1-8.2 (not listed in the canvas links, but loosely related covering Grids)

    • Chapter 14: Markov Decision Processes

    • Chapter 15: Partially Observable Markov Decision Processes

  5. PID Control

  6. SLAM

    • Chapter 10: Simultaneous Localization and Mapping

    • Chapter 11: The GraphSLAM Algorithm

    • Chapter 13: The FastSLAM Algorithm

# Detailed Notes