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.
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About this Course
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- Particle Filter
- Estimation
- Mapping
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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
The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.
Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
The course is too difficult and the class is too short to understand, I have to spend a lot of this learn the knowledge needed in other place.
About the Robotics Specialization

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