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.
WEEK 4 Introduction
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Skills You'll Learn
Particle Filter, Estimation, Mapping
Reviews
4.3 (494 ratings)
- 5 stars58.50%
- 4 stars20.85%
- 3 stars12.34%
- 2 stars4.04%
- 1 star4.25%
DA
Feb 5, 2021
This course was interesting but I think the video material was too shallow and not detailed enough. The assignment for Week 4 was extremely challenging!
NN
Jun 19, 2016
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
From the lesson
Bayesian Estimation - Localization
We will learn about robotic localization. Specifically, our goal of this week is to understand a how range measurements, coupled with odometer readings, can place a robot on a map. Later in the week, we introduce 3D localization as well.
Taught By
Daniel Lee
Professor of Electrical and Systems Engineering
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