About this course: 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.
Created by:Â Â University of Pennsylvania
Taught by:Â Â Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| Basic Info | Course 5 of 6 in the Robotics Specialization |
| Commitment | 4 weeks of study, 3-4 hours/week |
| Language | English, Subtitles: Chinese (Simplified) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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, 3 readings
- Reading: MATLAB Tutorial - Getting Started with MATLAB
- Reading: Setting Up your MATLAB Environment
- Reading: Basic Probability
- Video: WEEK 1 Introduction
- Video: 1.2.1. 1D Gaussian Distribution
- Video: 1.2.2. Maximum Likelihood Estimate (MLE)
- Video: 1.3.1. Multivariate Gaussian Distribution
- Video: 1.3.2. MLE of Multivariate Gaussian
- Video: 1.4.1. Gaussian Mixture Model (GMM)
- Video: 1.4.2. GMM Parameter Estimation via EM
- Video: 1.4.3. Expectation-Maximization (EM)
Graded: Color Learning and Target Detection
WEEK 2
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
- Video: Kalman Filter Motivation
- Video: System and Measurement Models
- Video: Maximum-A-Posterior Estimation
- Video: Extended Kalman Filter and Unscented Kalman Filter
Graded: Kalman Filter Tracking
WEEK 3
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
- Video: Introduction to Mapping
- Video: 3.2.1. Occupancy Grid Map
- Video: 3.2.2. Log-odd Update
- Video: 3.2.3. Handling Range Sensor
- Video: Introduction to 3D Mapping
Graded: 2D Occupancy Grid Mapping
WEEK 4
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
- Video: Odometry Modeling
- Video: Map Registration
- Video: Particle Filter
- Video: Iterative Closest Point
- Video: Closing
Graded: Particle Filter Based Localization
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Creators
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.
Pricing
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| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
The assignment is not designed very well especially the last one. Lacking of lots of details.
First 2 Week was very good but I think Mapping and Localization parts were not covered throughly. There should have been more detail.
Good materials for beginners. Assignments are interesting and useful.
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good course ..expecting more follow up courses on this topic !