How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.
Offered By
About this Course
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- Particle Filter
- Estimation
- Mapping
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Gaussian Model Learning
Bayesian Estimation - Target Tracking
Mapping
Bayesian Estimation - Localization
Reviews
- 5 stars58.50%
- 4 stars20.85%
- 3 stars12.34%
- 2 stars4.04%
- 1 star4.25%
TOP REVIEWS FROM ROBOTICS: ESTIMATION AND LEARNING
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
Lesson 1 and Lesson 3 are clear. However, homework in Lesson 2 and Lesson 4 is hard to finish because of too few materials in the lesson. Overall, it is a fairly good course.
Excellent exposure to mapping, localization, etc. Would have liked to have odometry included in the week4 assignment.
About the Robotics Specialization

Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.