關於此課程：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。
製作方： University of Pennsylvania
教學方： Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| 基本信息 | 課程 5（共 6 門，Robotics Specialization ） |
| 承諾學習時間 | 学习时间：4周，每周3-4小时 |
| 語言 | English, 字幕：Chinese (Simplified) |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
授課大綱
第 1 週
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, 3 個閱讀材料
- Leyendo: MATLAB Tutorial - Getting Started with MATLAB
- Leyendo: Setting Up your MATLAB Environment
- Leyendo: Basic Probability
- 視頻: WEEK 1 Introduction
- 視頻: 1.2.1. 1D Gaussian Distribution
- 視頻: 1.2.2. Maximum Likelihood Estimate (MLE)
- 視頻: 1.3.1. Multivariate Gaussian Distribution
- 視頻: 1.3.2. MLE of Multivariate Gaussian
- 視頻: 1.4.1. Gaussian Mixture Model (GMM)
- 視頻: 1.4.2. GMM Parameter Estimation via EM
- 視頻: 1.4.3. Expectation-Maximization (EM)
已評分: Color Learning and Target Detection
第 2 週
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
- 視頻: Kalman Filter Motivation
- 視頻: System and Measurement Models
- 視頻: Maximum-A-Posterior Estimation
- 視頻: Extended Kalman Filter and Unscented Kalman Filter
已評分: Kalman Filter Tracking
第 3 週
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
- 視頻: Introduction to Mapping
- 視頻: 3.2.1. Occupancy Grid Map
- 視頻: 3.2.2. Log-odd Update
- 視頻: 3.2.3. Handling Range Sensor
- 視頻: Introduction to 3D Mapping
已評分: 2D Occupancy Grid Mapping
第 4 週
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
- 視頻: Odometry Modeling
- 視頻: Map Registration
- 視頻: Particle Filter
- 視頻: Iterative Closest Point
- 視頻: Closing
已評分: Particle Filter Based Localization
常見問題解答
運作方式
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
製作方
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.
價格
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
評分和審閱
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 !!