Probabilistic Robotics

Master the math behind uncertainty to build robots that navigate and act reliably using Kalman filters, particle filters, and simultaneous localization and mapping in unpredictable environments. * **Master uncertainty modeling** for robust robotic decision-making * **Implement Kalman and particle filters** for precise state estimation * **Build SLAM systems** for autonomous mapping and localization * **Develop reliable control strategies** despite noisy sensor data

9 sections ยท 36 lessons

Course outline

Foundations

  1. Why Uncertainty Matters
  2. Probability Basics
  3. Bayes' Theorem
  4. The Bayes Filter
  5. Prediction and Update
  6. Foundations

Gaussian Filters

  1. Kalman Filter Basics
  2. Kalman Filter Equations
  3. Extended Kalman Filter
  4. EKF Implementation
  5. Unscented Kalman Filter
  6. Information Filter

Nonparametric Filters

  1. Histogram Filters
  2. Particle Filter Concept
  3. Particle Filter Algorithm
  4. Resampling Strategies
  5. Adaptive Resampling

Robot Models

  1. Motion Model Fundamentals
  2. Odometry Motion Model
  3. Beam Sensor Model
  4. Likelihood Field Model

Localization

  1. Localization Problem Types
  2. EKF Localization
  3. Monte Carlo Localization
  4. Adaptive MCL

Mapping

  1. Occupancy Grid Concept
  2. Binary Bayes Filter for Maps

SLAM Fundamentals

  1. The SLAM Problem
  2. Data Association

SLAM Algorithms

  1. EKF SLAM Concept
  2. EKF SLAM Limitations
  3. FastSLAM
  4. Graph-Based SLAM
  5. Graph SLAM Front-End and Back-End

Planning

  1. POMDPs Introduction
  2. Exploration and Information Gain
  3. Practical POMDP Solutions

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