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.
课程信息
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Learner Career Outcomes
40%
完成这些课程后已开始新的职业生涯
43%
通过此课程获得实实在在的工作福利
20%
加薪或升职
您将获得的技能
Particle FilterEstimationMapping
Learner Career Outcomes
40%
完成这些课程后已开始新的职业生涯
43%
通过此课程获得实实在在的工作福利
20%
加薪或升职
100% 在线
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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）, 1 个测验
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）, 1 个测验
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）, 1 个测验
6 个视频
Odometry Modeling5分钟
Map Registration5分钟
Particle Filter4分钟
Iterative Closest Point5分钟
Closing45
4.3
92 条评论
来自Robotics: Estimation and Learning的热门评论
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.
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.
讲师
Daniel Lee
Professor of Electrical and Systems EngineeringSchool of Engineering and Applied Science
关于 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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