About this Course
4.2
299 ratings
77 reviews
Specialization

Course 5 of 6 in the

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100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 13 hours to complete

Suggested: 4 weeks of study, 3-4 hours/week...
Available languages

English

Subtitles: English, Chinese (Simplified)

Skills you will gain

Particle FilterEstimationMapping
Specialization

Course 5 of 6 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 13 hours to complete

Suggested: 4 weeks of study, 3-4 hours/week...
Available languages

English

Subtitles: English, Chinese (Simplified)

Syllabus - What you will learn from this course

Week
1
Hours to complete
4 hours to complete

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....
Reading
9 videos (Total 52 min), 3 readings, 1 quiz
Video9 videos
WEEK 1 Introduction1m
1.2.1. 1D Gaussian Distribution8m
1.2.2. Maximum Likelihood Estimate (MLE)6m
1.3.1. Multivariate Gaussian Distribution7m
1.3.2. MLE of Multivariate Gaussian4m
1.4.1. Gaussian Mixture Model (GMM)4m
1.4.2. GMM Parameter Estimation via EM7m
1.4.3. Expectation-Maximization (EM)6m
Reading3 readings
MATLAB Tutorial - Getting Started with MATLAB10m
Setting Up your MATLAB Environment10m
Basic Probability10m
Week
2
Hours to complete
3 hours to complete

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....
Reading
5 videos (Total 21 min), 1 quiz
Video5 videos
Kalman Filter Motivation4m
System and Measurement Models5m
Maximum-A-Posterior Estimation4m
Extended Kalman Filter and Unscented Kalman Filter4m
Week
3
Hours to complete
4 hours to complete

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....
Reading
6 videos (Total 36 min), 1 quiz
Video6 videos
Introduction to Mapping7m
3.2.1. Occupancy Grid Map6m
3.2.2. Log-odd Update6m
3.2.3. Handling Range Sensor6m
Introduction to 3D Mapping8m
Week
4
Hours to complete
3 hours to complete

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....
Reading
6 videos (Total 23 min), 1 quiz
Video6 videos
Odometry Modeling5m
Map Registration5m
Particle Filter4m
Iterative Closest Point5m
Closing45s
4.2
77 ReviewsChevron Right
Career direction

67%

started a new career after completing these courses
Career Benefit

60%

got a tangible career benefit from this course
Career promotion

25%

got a pay increase or promotion

Top Reviews

By VGFeb 16th 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.

By NNJun 20th 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 .

Instructor

Avatar

Daniel Lee

Professor of Electrical and Systems Engineering
School of Engineering and Applied Science

About 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. ...

About the Robotics Specialization

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....
Robotics

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.