关于此课程： 机器人如何实时确定他们的状态，并从带有噪声的传感器测量量获得周围环境的信息？在这个模块中，你将学习怎样让机器人把不确定性融入估计，并向动态和变化的世界进行学习。特殊专题包括用于定位和绘图的概率生成模型和贝叶斯滤波器。
制作方： University of Pennsylvania
教学方： Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| 基本信息 | 课程 5（共 6 门，Robotics Specialization ） |
| 承诺学习时间 | 学习时间：4周，每周3-4小时 |
| 语言 | 英语（English）, 字幕： 中文（简体） |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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, 3 个阅读材料
- Reading: MATLAB Tutorial - Getting Started with MATLAB
- Reading: Setting Up your MATLAB Environment
- Reading: Basic Probability
- 视频: WEEK 1 Introduction
- 视频: 1.2.1. 1D Gaussian Distribution
- 视频: 1.2.2. Maximum Likelihood Estimate (MLE)
- 视频: 1.3.1. Multivariate Gaussian Distribution
- 视频: 1.3.2. MLE of Multivariate Gaussian
- 视频: 1.4.1. Gaussian Mixture Model (GMM)
- 视频: 1.4.2. GMM Parameter Estimation via EM
- 视频: 1.4.3. Expectation-Maximization (EM)
已评分: Color Learning and Target Detection
第 2 周
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
- 视频: Kalman Filter Motivation
- 视频: System and Measurement Models
- 视频: Maximum-A-Posterior Estimation
- 视频: Extended Kalman Filter and Unscented Kalman Filter
已评分: Kalman Filter Tracking
第 3 周
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
- 视频: Introduction to Mapping
- 视频: 3.2.1. Occupancy Grid Map
- 视频: 3.2.2. Log-odd Update
- 视频: 3.2.3. Handling Range Sensor
- 视频: Introduction to 3D Mapping
已评分: 2D Occupancy Grid Mapping
第 4 周
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
- 视频: Odometry Modeling
- 视频: Map Registration
- 视频: Particle Filter
- 视频: Iterative Closest Point
- 视频: Closing
已评分: Particle Filter Based Localization
常见问题解答
运作方式
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
制作方
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.
价格
| 购买课程 | |
|---|---|
| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
good course ..expecting more follow up courses on this topic !
The assignment is not designed very well especially the last one. Lacking of lots of details.
AA
Great course learnt a lot !!