Informações sobre o curso: 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.
Desenvolvido por: University of Pennsylvania
Ministrado por: Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| Informações básicas | Curso 5 de 6 no Robotics Specialization |
| Compromisso | 4 weeks of study, 3-4 hours/week |
| Idioma | English, Legendas: Chinese (Simplified) |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 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
- Leitura: MATLAB Tutorial - Getting Started with MATLAB
- Leitura: Setting Up your MATLAB Environment
- Leitura: Basic Probability
- Vídeo: WEEK 1 Introduction
- Vídeo: 1.2.1. 1D Gaussian Distribution
- Vídeo: 1.2.2. Maximum Likelihood Estimate (MLE)
- Vídeo: 1.3.1. Multivariate Gaussian Distribution
- Vídeo: 1.3.2. MLE of Multivariate Gaussian
- Vídeo: 1.4.1. Gaussian Mixture Model (GMM)
- Vídeo: 1.4.2. GMM Parameter Estimation via EM
- Vídeo: 1.4.3. Expectation-Maximization (EM)
Nota atribuída: Color Learning and Target Detection
SEMANA 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
- Vídeo: Kalman Filter Motivation
- Vídeo: System and Measurement Models
- Vídeo: Maximum-A-Posterior Estimation
- Vídeo: Extended Kalman Filter and Unscented Kalman Filter
Nota atribuída: Kalman Filter Tracking
SEMANA 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
- Vídeo: Introduction to Mapping
- Vídeo: 3.2.1. Occupancy Grid Map
- Vídeo: 3.2.2. Log-odd Update
- Vídeo: 3.2.3. Handling Range Sensor
- Vídeo: Introduction to 3D Mapping
Nota atribuída: 2D Occupancy Grid Mapping
SEMANA 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
- Vídeo: Odometry Modeling
- Vídeo: Map Registration
- Vídeo: Particle Filter
- Vídeo: Iterative Closest Point
- Vídeo: Closing
Nota atribuída: Particle Filter Based Localization
Perguntas frequentes
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Trabalho
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Desenvolvedores
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.
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
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