About this course: 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。
Created by: University of Pennsylvania
Taught by: Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| Basic Info | Course 5 of 6 in the Robotics Specialization |
| Commitment | 学习时间：4周，每周3-4小时 |
| Language | English, Subtitles: Chinese (Simplified) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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 videos, 3 readings
- Leyendo: MATLAB Tutorial - Getting Started with MATLAB
- Leyendo: Setting Up your MATLAB Environment
- Leyendo: Basic Probability
- Video: WEEK 1 Introduction
- Video: 1.2.1. 1D Gaussian Distribution
- Video: 1.2.2. Maximum Likelihood Estimate (MLE)
- Video: 1.3.1. Multivariate Gaussian Distribution
- Video: 1.3.2. MLE of Multivariate Gaussian
- Video: 1.4.1. Gaussian Mixture Model (GMM)
- Video: 1.4.2. GMM Parameter Estimation via EM
- Video: 1.4.3. Expectation-Maximization (EM)
Graded: Color Learning and Target Detection
WEEK 2
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 videos
- Video: Kalman Filter Motivation
- Video: System and Measurement Models
- Video: Maximum-A-Posterior Estimation
- Video: Extended Kalman Filter and Unscented Kalman Filter
Graded: Kalman Filter Tracking
WEEK 3
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 videos
- Video: Introduction to Mapping
- Video: 3.2.1. Occupancy Grid Map
- Video: 3.2.2. Log-odd Update
- Video: 3.2.3. Handling Range Sensor
- Video: Introduction to 3D Mapping
Graded: 2D Occupancy Grid Mapping
WEEK 4
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 videos
- Video: Odometry Modeling
- Video: Map Registration
- Video: Particle Filter
- Video: Iterative Closest Point
- Video: Closing
Graded: Particle Filter Based Localization
FAQs
How It Works
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.
Creators
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.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
The assignment is not designed very well especially the last one. Lacking of lots of details.
First 2 Week was very good but I think Mapping and Localization parts were not covered throughly. There should have been more detail.
Good materials for beginners. Assignments are interesting and useful.
.
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2018 Coursera Inc. All rights reserved.
good course ..expecting more follow up courses on this topic !