Robotics: Estimation and Learning
University of Pennsylvania via Coursera
Social Learning for Open Courses
Join one of our cohorts to learn from instructors, experts, or each other with weekly live streams.
-
University of Alberta
Science Friday: Black HolesBlack holes are fascinating and intriguing astronomical objects that capture our imagination. Astro 101, from University of Alberta, teaches us concepts such as astronomy, relativity, quantum physics, and much more.
Jan 03, 20225 weeks
-
Coursera
Uncommon Sense TeachingA Cohort with the acclaimed Instructor Barbara Oakley with 4+ million learners worldwide. This course weaves late-breaking insights from neuroscience with practical new approaches from the classroom.
Jan 17, 20224 weeks
Overview
Class Central Tips
Syllabus
- 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.
- Bayesian Estimation - Target Tracking
- We will learn about the Gaussian distribution for tracking a dynamical system. We will start by discussing the dynamical systems and their impact on probability distributions. This linear Kalman filter system will be described in detail, and, in addition, non-linear filtering systems will be explored.
- Mapping
- We will learn about robotic mapping. Specifically, our goal of this week is to understand a mapping algorithm called Occupancy Grid Mapping based on range measurements. Later in the week, we introduce 3D mapping as well.
- 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
Dan Lee
Tags
Related Courses
Reviews
2.0 rating, based on 1 reviews
-
Alex Ivanov
27Alex Ivanov completed this course.
Never Stop Learning!
