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.
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コース終了後に新しいキャリアをスタートした
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Particle FilterEstimationMapping
受講生の就業成果
40%
コース終了後に新しいキャリアをスタートした
43%
コースが具体的なキャリアアップにつながった
20%
昇給や昇進につながった
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自分のスケジュールですぐに学習を始めてください。
次における6の5コース
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約12時間で修了
推奨：4 weeks of study, 3-4 hours/week
英語
字幕：英語, スペイン語, 中国語（簡体）
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ペンシルベニア大学（University of Pennsylvania）
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.
シラバス - 本コースの学習内容
4時間で修了
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時間で修了
9件のビデオ （合計52分）, 3 readings, 1 quiz
9件のビデオ
WEEK 1 Introduction1 分
1.2.1. 1D Gaussian Distribution8 分
1.2.2. Maximum Likelihood Estimate (MLE)6 分
1.3.1. Multivariate Gaussian Distribution7 分
1.3.2. MLE of Multivariate Gaussian4 分
1.4.1. Gaussian Mixture Model (GMM)4 分
1.4.2. GMM Parameter Estimation via EM7 分
1.4.3. Expectation-Maximization (EM)6 分
3件の学習用教材
MATLAB Tutorial - Getting Started with MATLAB10 分
Setting Up your MATLAB Environment10 分
Basic Probability10 分
3時間で修了
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時間で修了
5件のビデオ （合計21分）
5件のビデオ
Kalman Filter Motivation4 分
System and Measurement Models5 分
Maximum-A-Posterior Estimation4 分
Extended Kalman Filter and Unscented Kalman Filter4 分
4時間で修了
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時間で修了
6件のビデオ （合計36分）
6件のビデオ
Introduction to Mapping7 分
3.2.1. Occupancy Grid Map6 分
3.2.2. Log-odd Update6 分
3.2.3. Handling Range Sensor6 分
Introduction to 3D Mapping8 分
3時間で修了
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時間で修了
6件のビデオ （合計23分）
6件のビデオ
Odometry Modeling5 分
Map Registration5 分
Particle Filter4 分
Iterative Closest Point5 分
Closing45
レビュー
4.3
ROBOTICS: ESTIMATION AND LEARNING からの人気レビュー
SSによりApr 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.
SJによりAug 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.
VGによりFeb 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.
NNによりJun 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 .
LZによりNov 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.
AVによりJun 24, 2016
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
ASによりNov 22, 2019
Really good course. Engaging and relevant content. The assignments push you but test your fundamentals and you end up learning a lot.
HAによりJun 6, 2019
Course content needs researching on the internet as well. And course assignments are good learning experience but need research too.
RAによりAug 20, 2017
Very interesting and useful course. Professors give a lot of information. However, some explanations are not very clear.
DDによりJan 6, 2019
Excellent exposure to mapping, localization, etc. Would have liked to have odometry included in the week4 assignment.
ABによりFeb 11, 2019
It was a well timed course with short videos. However, the assignments didn't do justice (especially assignment 4)
RSによりApr 22, 2017
Pretty practical course It' ll involve a good amount of programming. Not quiz and theoretical verification here.
ロボット工学専門講座について
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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