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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/robotics-learning
课程评论：没有评论
课程名称：机器人技术：估计与学习 课程概述：本模块将探讨机器人如何从嘈杂的传感器测量中确定其状态和周围环境的性质。学员将学习如何让机器人在一个动态变化的世界中引入不确定性，以进行估计和学习。涵盖的具体主题包括概率生成模型、用于定位和地图绘制的贝叶斯过滤。 课程大纲： 第一部分：高斯模型学习 描述：我们将学习高斯分布在机器人中的参数建模应用。高斯分布是最常用的连续分布，能够有效地估计不确定性并进行世界预测。课程将从一维高斯分布入手，然后转向多变量高斯分布，最后扩展到使用高斯混合模型。 第二部分：贝叶斯估计 - 目标跟踪 描述：我们将探讨高斯分布在动态系统跟踪中的应用。课程将详细讨论动态系统对概率分布的影响，介绍线性卡尔曼滤波器系统，并探讨非线性滤波系统。 第三部分：地图绘制 描述：我们将学习机器人地图绘制的相关知识。本周的目标是理解基于距离测量的占用网格地图绘制算法，随后也将介绍3D地图绘制。 第四部分：贝叶斯估计 - 本地化 描述：我们将研究机器人的本地化技术。具体目标是理解通过距离测量结合里程计读数如何在地图上定位机器人，随后引入3D本地化的概念。
Part: 1
Title:Gaussian Model Learning
Description: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.
Part: 2
Title:Bayesian Estimation - Target Tracking
Description: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.
Part: 3
Title:Mapping
Description: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.
Part: 4
Title:Bayesian Estimation - Localization
Description: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.
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.
