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.
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 4 小時
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 個視頻 （總計 52 分鐘）, 3 個閱讀材料, 1 個測驗
9 個視頻
1.2.2. Maximum Likelihood Estimate (MLE)6分鐘
1.3.1. Multivariate Gaussian Distribution7分鐘
1.3.2. MLE of Multivariate Gaussian4分鐘
1.4.1. Gaussian Mixture Model (GMM)4分鐘
1.4.2. GMM Parameter Estimation via EM7分鐘
1.4.3. Expectation-Maximization (EM)6分鐘
3 個閱讀材料
MATLAB Tutorial - Getting Started with MATLAB10分鐘
Setting Up your MATLAB Environment10分鐘
Basic Probability10分鐘
週
2完成時間為 3 小時
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 個視頻 （總計 21 分鐘）, 1 個測驗
5 個視頻
Maximum-A-Posterior Estimation4分鐘
Extended Kalman Filter and Unscented Kalman Filter4分鐘
週
3完成時間為 4 小時
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 個視頻 （總計 36 分鐘）, 1 個測驗
6 個視頻
3.2.2. Log-odd Update6分鐘
3.2.3. Handling Range Sensor6分鐘
Introduction to 3D Mapping8分鐘
週
4完成時間為 3 小時
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 個視頻 （總計 23 分鐘）, 1 個測驗
6 個視頻
Particle Filter4分鐘
Iterative Closest Point5分鐘
Closing45
4.2
85 個審閱50%
完成這些課程後已開始新的職業生涯
50%
通過此課程獲得實實在在的工作福利
20%
加薪或升職
來自Robotics: Estimation and Learning的熱門評論
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 .
講師
Daniel Lee
Professor of Electrical and Systems EngineeringSchool of Engineering and Applied Science
關於 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.
關於 机器人 專項課程
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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