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.
教学大纲 - 您将从这门课程中学到什么
周
1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.
9 个视频 （总计 52 分钟）, 3 个阅读材料, 1 个测验
9 个视频
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
周
2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.
5 个视频 （总计 21 分钟）, 1 个测验
5 个视频
Maximum-A-Posterior Estimation4分钟
Extended Kalman Filter and Unscented Kalman Filter4分钟
周
3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.
6 个视频 （总计 36 分钟）, 1 个测验
6 个视频
3.2.2. Log-odd Update6分钟
3.2.3. Handling Range Sensor6分钟
Introduction to 3D Mapping8分钟
周
4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.
6 个视频 （总计 23 分钟）, 1 个测验
6 个视频
Particle Filter4分钟
Iterative Closest Point5分钟
Closing45
4.2
85 个审阅50%
完成这些课程后已开始新的职业生涯
50%
通过此课程获得实实在在的工作福利
20%
加薪或升职
来自Robotics: Estimation and Learning的热门评论
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.
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
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.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2019 Coursera Inc. 保留所有权利。
