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 1 Introduction
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University of Pennsylvania
Course 5 of 6 in the Robotics Specialization
Skills You'll Learn
Particle Filter, Estimation, Mapping
Reviews
NN
Jun 20, 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 .
AV
Jun 25, 2016
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
From the lesson
Gaussian Model Learning
We will learn about the Gaussian distribution for parametric modeling in robotics. The Gaussian distribution is the most widely used continuous distribution and provides a useful way to estimate uncertainty and predict in the world. We will start by discussing the one-dimensional Gaussian distribution, and then move on to the multivariate Gaussian distribution. Finally, we will extend the concept to models that use Mixtures of Gaussians.
Taught By
Daniel Lee
Professor of Electrical and Systems Engineering
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