4.2
331 ratings
84 reviews

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 13 hours to complete

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

#### English

Subtitles: English, Chinese (Simplified)

### Skills you will gain

Particle FilterEstimationMapping

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 13 hours to complete

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

#### English

Subtitles: English, Chinese (Simplified)

### Syllabus - What you will learn from this course

Week
1
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....
9 videos (Total 52 min), 3 readings, 1 quiz
9 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
MATLAB Tutorial - Getting Started with MATLAB10m
Basic Probability10m
Week
2
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....
5 videos (Total 21 min), 1 quiz
5 videos
Kalman Filter Motivation4m
System and Measurement Models5m
Maximum-A-Posterior Estimation4m
Extended Kalman Filter and Unscented Kalman Filter4m
Week
3
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....
6 videos (Total 36 min), 1 quiz
6 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
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....
6 videos (Total 23 min), 1 quiz
6 videos
Odometry Modeling5m
Map Registration5m
Particle Filter4m
Iterative Closest Point5m
Closing45s
4.2
84 Reviews

## 50%

started a new career after completing these courses

## 50%

got a tangible career benefit from this course

## 20%

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

### Daniel Lee

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

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. ...