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
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
Week 1 and Week 3 are organized much better than Week 2 and Week 4. If you don't have enough time, I recommend that you focus on Week 1 and 3.
Very succinct lectures which provides necessary foundation to learn advanced localization algorithms.
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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