Probabilistic Robotics

Master how robots handle real-world uncertainty using simple math and logic to turn your curiosity into a foundational understanding of intelligent machines. * **Robot uncertainty management** using basic probability and logic * **Sensor data interpretation** to understand how robots see * **Map building basics** for navigating unknown environments * **Decision making models** for autonomous robot movement

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

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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