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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
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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
- Udacity Course: Artificial Intelligence for Robotics
- Coursera: Robotics - Estimation and Learning
- Probabilistic Robotics
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原文地址:https://www.cnblogs.com/casperwin/p/7580024.html
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