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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From the course by University of Pennsylvania
Robotics: Estimation and Learning
University of Pennsylvania
Course 5 of 6 in the Specialization Robotics
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.
Meet the Instructors
Daniel LeeProfessor of Electrical and Systems Engineering
School of Engineering and Applied Science
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