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.
This course is part of the Robotics Specialization
About this Course
Skills you will gain
- Particle Filter
Syllabus - What you will learn from this course
Gaussian Model Learning
Bayesian Estimation - Target Tracking
Bayesian Estimation - Localization
- 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
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