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.
Este curso faz parte do Programa de cursos integrados Robótica
Robotics: Estimation and Learning
oferecido por
Informações sobre o curso
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Habilidades que você terá
Particle FilterEstimationMapping
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
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.
9 vídeos ((Total 52 mín.)), 3 leituras, 1 teste
9 videos
WEEK 1 Introduction1min
1.2.1. 1D Gaussian Distribution8min
1.2.2. Maximum Likelihood Estimate (MLE)6min
1.3.1. Multivariate Gaussian Distribution7min
1.3.2. MLE of Multivariate Gaussian4min
1.4.1. Gaussian Mixture Model (GMM)4min
1.4.2. GMM Parameter Estimation via EM7min
1.4.3. Expectation-Maximization (EM)6min
3 leituras
MATLAB Tutorial - Getting Started with MATLAB10min
Setting Up your MATLAB Environment10min
Basic Probability10min
Semana
23 horas para concluir
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.
5 vídeos ((Total 21 mín.)), 1 teste
5 videos
Kalman Filter Motivation4min
System and Measurement Models5min
Maximum-A-Posterior Estimation4min
Extended Kalman Filter and Unscented Kalman Filter4min
Semana
34 horas para concluir
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.
6 vídeos ((Total 36 mín.)), 1 teste
6 videos
Introduction to Mapping7min
3.2.1. Occupancy Grid Map6min
3.2.2. Log-odd Update6min
3.2.3. Handling Range Sensor6min
Introduction to 3D Mapping8min
Semana
43 horas para concluir
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.
6 vídeos ((Total 23 mín.)), 1 teste
6 videos
Odometry Modeling5min
Map Registration5min
Particle Filter4min
Iterative Closest Point5min
Closing45s
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Principais avaliações do Robotics: Estimation and Learning
The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
Instrutores
Daniel Lee
Professor of Electrical and Systems EngineeringSchool of Engineering and Applied Science
Sobre Universidade da Pensilvânia
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Sobre o Programa de cursos integrados Robótica
The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects.
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