Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course.

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## 課程信息

### 您將學到的內容有

Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares

Develop a model for typical vehicle localization sensors, including GPS and IMUs

Apply extended and unscented Kalman Filters to a vehicle state estimation problem

Apply LIDAR scan matching and the Iterative Closest Point algorithm

#### 100% 在線

#### 第 2 門課程（共 4 門）

#### 可靈活調整截止日期

#### 高級

This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.

#### 完成時間大約為23 小時

#### 英語（English）

## 教學大綱 - 您將從這門課程中學到什麼

**完成時間為 2 小時**

## Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars

This module introduces you to the main concepts discussed in the course and presents the layout of the course. The module describes and motivates the problems of state estimation and localization for self-driving cars.

**完成時間為 2 小時**

**9 個視頻**

**3 個閱讀材料**

**完成時間為 7 小時**

## Module 1: Least Squares

The method of least squares, developed by Carl Friedrich Gauss in 1795, is a well known technique for estimating parameter values from data. This module provides a review of least squares, for the cases of unweighted and weighted observations. There is a deep connection between least squares and maximum likelihood estimators (when the observations are considered to be Gaussian random variables) and this connection is established and explained. Finally, the module develops a technique to transform the traditional 'batch' least squares estimator to a recursive form, suitable for online, real-time estimation applications.

**完成時間為 7 小時**

**4 個視頻**

**3 個閱讀材料**

**3 個練習**

**完成時間為 7 小時**

## Module 2: State Estimation - Linear and Nonlinear Kalman Filters

Any engineer working on autonomous vehicles must understand the Kalman filter, first described in a paper by Rudolf Kalman in 1960. The filter has been recognized as one of the top 10 algorithms of the 20th century, is implemented in software that runs on your smartphone and on modern jet aircraft, and was crucial to enabling the Apollo spacecraft to reach the moon. This module derives the Kalman filter equations from a least squares perspective, for linear systems. The module also examines why the Kalman filter is the best linear unbiased estimator (that is, it is optimal in the linear case). The Kalman filter, as originally published, is a linear algorithm; however, all systems in practice are nonlinear to some degree. Shortly after the Kalman filter was developed, it was extended to nonlinear systems, resulting in an algorithm now called the ‘extended’ Kalman filter, or EKF. The EKF is the ‘bread and butter’ of state estimators, and should be in every engineer’s toolbox. This module explains how the EKF operates (i.e., through linearization) and discusses its relationship to the original Kalman filter. The module also provides an overview of the unscented Kalman filter, a more recently developed and very popular member of the Kalman filter family.

**完成時間為 7 小時**

**6 個視頻**

**5 個閱讀材料**

**完成時間為 2 小時**

## Module 3: GNSS/INS Sensing for Pose Estimation

To navigate reliably, autonomous vehicles require an estimate of their pose (position and orientation) in the world (and on the road) at all times. Much like for modern aircraft, this information can be derived from a combination of GPS measurements and inertial navigation system (INS) data. This module introduces sensor models for inertial measurement units and GPS (and, more broadly, GNSS) receivers; performance and noise characteristics are reviewed. The module describes ways in which the two sensor systems can be used in combination to provide accurate and robust vehicle pose estimates.

**完成時間為 2 小時**

**4 個視頻**

**3 個閱讀材料**

**1 個練習**

**完成時間為 2 小時**

## Module 4: LIDAR Sensing

LIDAR (light detection and ranging) sensing is an enabling technology for self-driving vehicles. LIDAR sensors can ‘see’ farther than cameras and are able to provide accurate range information. This module develops a basic LIDAR sensor model and explores how LIDAR data can be used to produce point clouds (collections of 3D points in a specific reference frame). Learners will examine ways in which two LIDAR point clouds can be registered, or aligned, in order to determine how the pose of the vehicle has changed with time (i.e., the transformation between two local reference frames).

**完成時間為 2 小時**

**4 個視頻**

**3 個閱讀材料**

**1 個練習**

### 來自State Estimation and Localization for Self-Driving Cars的熱門評論

There are many interesting topics. Without the help and suggested readings from this course, I wouldn't be able to finish by myself. Also, the final project is very enlightening.

One of the most exciting courses ever had in terms of learning and understanding. Kalman filter is a fascinating concept with infinite applications in real life on daily basis.

### 關於 多伦多大学

## 關於 自动驾驶汽车 專項課程

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