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
15,353 visualizações recentes
Resultados de carreira do aprendiz
40%
comecei uma nova carreira após concluir estes cursos
43%
consegui um benefício significativo de carreira com este curso
20%
recebi um aumento ou promoção
Habilidades que você terá
Particle FilterEstimationMapping
Resultados de carreira do aprendiz
40%
comecei uma nova carreira após concluir estes cursos
43%
consegui um benefício significativo de carreira com este curso
20%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 5 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 12 horas para completar
Sugerido: 4 weeks of study, 3-4 hours/week
Inglês
Legendas: Inglês, Espanhol, Chinês (simplificado)
oferecido por
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.
Programa - O que você aprenderá com este curso
4 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.
4 horas para concluir
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
3 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.
3 horas para concluir
5 vídeos (Total 21 mín.)
5 videos
Kalman Filter Motivation4min
System and Measurement Models5min
Maximum-A-Posterior Estimation4min
Extended Kalman Filter and Unscented Kalman Filter4min
4 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.
4 horas para concluir
6 vídeos (Total 36 mín.)
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
3 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.
3 horas para concluir
6 vídeos (Total 23 mín.)
6 videos
Odometry Modeling5min
Map Registration5min
Particle Filter4min
Iterative Closest Point5min
Closing45s
Avaliações
Principais avaliações do ROBOTICS: ESTIMATION AND LEARNING
por SSApr 6, 2017
Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.
por SJAug 6, 2016
Lesson 1 and Lesson 3 are clear. However, homework in Lesson 2 and Lesson 4 is hard to finish because of too few materials in the lesson. Overall, it is a fairly good course.
por VGFeb 15, 2017
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.
por NNJun 19, 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 .
por LZNov 26, 2016
Week 1 and Week 3 are organized much better than Week 2 and Week 4. If you don't have enough time, I recommend that you focus on Week 1 and 3.
por AVJun 24, 2016
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
por ASNov 22, 2019
Really good course. Engaging and relevant content. The assignments push you but test your fundamentals and you end up learning a lot.
por HAJun 6, 2019
Course content needs researching on the internet as well. And course assignments are good learning experience but need research too.
por RAAug 20, 2017
Very interesting and useful course. Professors give a lot of information. However, some explanations are not very clear.
por DDJan 6, 2019
Excellent exposure to mapping, localization, etc. Would have liked to have odometry included in the week4 assignment.
por ABFeb 11, 2019
It was a well timed course with short videos. However, the assignments didn't do justice (especially assignment 4)
por RSApr 22, 2017
Pretty practical course It' ll involve a good amount of programming. Not quiz and theoretical verification here.
por VMSep 18, 2018
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
por SGDec 13, 2016
It's a great course. Although the assignment is little tough, you will gain a lot after completing it.
por SGMar 2, 2018
Very succinct lectures which provides necessary foundation to learn advanced localization algorithms.
por TZApr 2, 2018
The assignment is not designed very well especially the last one. Lacking of lots of details.
por KKAug 28, 2019
week 2 and 4 needs more information. Yet great learning experience at affordable price.
por MZMar 24, 2018
Good materials for beginners. Assignments are interesting and useful.\n\n.
por DMJan 31, 2020
Excellent course in estimation and implementation of Kalman Filters.
por XGDec 15, 2018
the topic is interesting, but the videos seems a little bit short
Sobre 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.
