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。

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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。

Particle Filter, Estimation, Mapping

4.2（379 个评分）

- 5 stars218 ratings
- 4 stars84 ratings
- 3 stars44 ratings
- 2 stars15 ratings
- 1 star18 ratings

Jun 25, 2016

A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .

Sep 19, 2018

This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...

从本节课中

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.

#### Daniel Lee

Professor of Electrical and Systems Engineering