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Robotics: Estimation and Learning
МООК
en
Английский
Subtitles available
12 h
This content is rated 0 out of 5
- Self-paced
- Free Access
- Fee-based Certificate
- 4 Sequences
- Introductive Level
- Субтитры доступны на Chinese
Course details
Syllabus
- Week 1 - 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... - Week 2 - 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-... - Week 3 - 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. - Week 4 - 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.
Prerequisite
None.
Instructors
Daniel Lee
Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
Editor
L'université de Pennsylvanie (communément appelée Penn) fondée en 1740 est une université privée située à Philadelphie, en Pennsylvanie, aux États-Unis. Membre de l'Ivy League, Penn est la quatrième plus ancienne institution d'enseignement supérieur des États-Unis et se considère comme la première université des États-Unis à proposer des études de premier et de deuxième cycle.
Platform
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