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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Particle FilterEstimationMapping
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40%
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20%
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第 5 门课程（共 6 门）
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完成时间大约为12 小时
建议：4 weeks of study, 3-4 hours/week
英语（English）
字幕：英语（English）, 西班牙语（Spanish）, 中文（简体）
教学大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
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.
完成时间为 4 小时
9 个视频 （总计 52 分钟）, 3 个阅读材料, 1 个测验
9 个视频
WEEK 1 Introduction1分钟
1.2.1. 1D Gaussian Distribution8分钟
1.2.2. Maximum Likelihood Estimate (MLE)6分钟
1.3.1. Multivariate Gaussian Distribution7分钟
1.3.2. MLE of Multivariate Gaussian4分钟
1.4.1. Gaussian Mixture Model (GMM)4分钟
1.4.2. GMM Parameter Estimation via EM7分钟
1.4.3. Expectation-Maximization (EM)6分钟
3 个阅读材料
MATLAB Tutorial - Getting Started with MATLAB10分钟
Setting Up your MATLAB Environment10分钟
Basic Probability10分钟
完成时间为 3 小时
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.
完成时间为 3 小时
5 个视频 （总计 21 分钟）
5 个视频
Kalman Filter Motivation4分钟
System and Measurement Models5分钟
Maximum-A-Posterior Estimation4分钟
Extended Kalman Filter and Unscented Kalman Filter4分钟
完成时间为 4 小时
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.
完成时间为 4 小时
6 个视频 （总计 36 分钟）
6 个视频
Introduction to Mapping7分钟
3.2.1. Occupancy Grid Map6分钟
3.2.2. Log-odd Update6分钟
3.2.3. Handling Range Sensor6分钟
Introduction to 3D Mapping8分钟
完成时间为 3 小时
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.
完成时间为 3 小时
6 个视频 （总计 23 分钟）
6 个视频
Odometry Modeling5分钟
Map Registration5分钟
Particle Filter4分钟
Iterative Closest Point5分钟
Closing45
审阅
4.3
来自ROBOTICS: ESTIMATION AND LEARNING的热门评论
由 SS 提供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.
由 SJ 提供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.
由 VG 提供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.
由 NN 提供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 .
由 LZ 提供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.
由 AV 提供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 .
由 AS 提供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.
由 HA 提供Jun 6, 2019
Course content needs researching on the internet as well. And course assignments are good learning experience but need research too.
由 RA 提供Aug 20, 2017
Very interesting and useful course. Professors give a lot of information. However, some explanations are not very clear.
由 DD 提供Jan 6, 2019
Excellent exposure to mapping, localization, etc. Would have liked to have odometry included in the week4 assignment.
由 AB 提供Feb 11, 2019
It was a well timed course with short videos. However, the assignments didn't do justice (especially assignment 4)
由 RS 提供Apr 22, 2017
Pretty practical course It' ll involve a good amount of programming. Not quiz and theoretical verification here.
由 VM 提供Sep 18, 2018
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
由 SG 提供Dec 13, 2016
It's a great course. Although the assignment is little tough, you will gain a lot after completing it.
由 SG 提供Mar 2, 2018
Very succinct lectures which provides necessary foundation to learn advanced localization algorithms.
由 TZ 提供Apr 2, 2018
The assignment is not designed very well especially the last one. Lacking of lots of details.
由 KK 提供Aug 28, 2019
week 2 and 4 needs more information. Yet great learning experience at affordable price.
由 LV 提供Jun 12, 2016
Contents relevant, lectures well paced and clear and TA's very helpful and on it!
由 MZ 提供Mar 24, 2018
Good materials for beginners. Assignments are interesting and useful.\n\n.
由 DM 提供Jan 31, 2020
Excellent course in estimation and implementation of Kalman Filters.
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宾夕法尼亚大学
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.
关于 机器人 专项课程
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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