学习笔记 | Artificial Intelligence for Robotics

Artificial Intelligence for Robotics

Overview

• Lesson 1 - Localization Overview (9/22/2017 - 10/1/2017)
• Lesson 2 - Kalman Filters (10/3/2017 - )
• Lesson 3 - Particle Filters
• Lesson 4 - Search
• Lesson 5 - PID Control
• Lesson 6 - SLAM
• Exam
• Project

Note

1. Localization Overview

• Postierior
• Convolution
• Measurement update
• Localization
  • Belief = Probility by Normalization
  • Sense = Product of the following 
  • Move = Convolution (Addition)
• Bayes's Rule
• The Theroem of Total Probability
• Monte Carlo Localization
• histogram filters

2. Kalman Filters

• Kalman Filters vs Monte Carlo Localization

• Kalman Filters	Monte Carlo Localization
  continuous	discrete
  uni-modal	multi-modal
  Gaussian	Histogram

3. Particle Filters

4. Search

5. PID Control

6. SLAM

Practice Exam

Project

Reference

1. Udacity Course: Artificial Intelligence for Robotics
2. Coursera: Robotics - Estimation and Learning
3. Probabilistic Robotics