MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!
Syllabus
Lesson 1
Localization
- Localization
- Total Probability
- Uniform Distribution
- Probability After Sense
- Normalize Distribution
- Phit and Pmiss
- Sum of Probabilities
- Sense Function
- Exact Motion
- Move Function
- Bayes Rule
- Theorem of Total Probability
Lesson 2
Kalman Filters
- Gaussian Intro
- Variance Comparison
- Maximize Gaussian
- Measurement and Motion
- Parameter Update
- New Mean Variance
- Gaussian Motion
- Kalman Filter Code
- Kalman Prediction
- Kalman Filter Design
- Kalman Matrices
Lesson 3
Particle Filters
- Slate Space
- Belief Modality
- Particle Filters
- Using Robot Class
- Robot World
- Robot Particles
Lesson 4
Search
- Motion Planning
- Compute Cost
- Optimal Path
- First Search Program
- Expansion Grid
- Dynamic Programming
- Computing Value
- Optimal Policy
Lesson 5
PID Control
- Robot Motion
- Smoothing Algorithm
- Path Smoothing
- Zero Data Weight
- Pid Control
- Proportional Control
- Implement P Controller
- Oscillations
- Pd Controller
- Systematic Bias
- Pid Implementation
- Parameter Optimization
Lesson 6
SLAM (Simultaneous Localization and Mapping)
- Localization
- Planning
- Segmented Ste
- Fun with Parameters
- SLAM
- Graph SLAM
- Implementing Constraints
- Adding Landmarks
- Matrix Modification
- Untouched Fields
- Landmark Position
- Confident Measurements
- Implementing SLAM
Lesson 7
Runaway Robot Final Project
- FEATU
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.