## 課程信息

121,360 次近期查看

100% 在線

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

### 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

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

1

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

9 個視頻 （總計 33 分鐘）, 3 個閱讀材料
9 個視頻
Welcome to the Course3分鐘
Meet the Instructor, Jonathan Kelly2分鐘
Meet the Instructor, Steven Waslander5分鐘
Meet Diana, Firmware Engineer2分鐘
Meet Winston, Software Engineer3分鐘
Meet Andy, Autonomous Systems Architect2分鐘
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5分鐘
The Importance of State Estimation1分鐘
3 個閱讀材料
Course Prerequisites: Knowledge, Hardware & Software15分鐘
How to Use Discussion Forums15分鐘
How to Use Supplementary Readings in This Course15分鐘

## Module 1: Least Squares

4 個視頻 （總計 33 分鐘）, 3 個閱讀材料, 3 個測驗
4 個視頻
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6分鐘
Lesson 2: Recursive Least Squares7分鐘
Lesson 3: Least Squares and the Method of Maximum Likelihood8分鐘
3 個閱讀材料
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45分鐘
Lesson 2 Supplementary Reading: Recursive Least Squares30分鐘
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30分鐘
3 個練習
Lesson 1: Practice Quiz30分鐘
Lesson 2: Practice Quiz30分鐘
2

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

6 個視頻 （總計 53 分鐘）, 5 個閱讀材料, 1 個測驗
6 個視頻
Lesson 2: Kalman Filter and The Bias BLUEs5分鐘
Lesson 3: Going Nonlinear - The Extended Kalman Filter9分鐘
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6分鐘
Lesson 5: Limitations of the EKF7分鐘
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15分鐘
5 個閱讀材料
Lesson 1 Supplementary Reading: The Linear Kalman Filter45分鐘
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10分鐘
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45分鐘
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter1小時
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30分鐘
3

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

4 個視頻 （總計 34 分鐘）, 3 個閱讀材料, 1 個測驗
4 個視頻
Lesson 2: The Inertial Measurement Unit (IMU)10分鐘
Lesson 3: The Global Navigation Satellite Systems (GNSS)8分鐘
Why Sensor Fusion?3分鐘
3 個閱讀材料
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10分鐘
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30分鐘
1 個練習
4

## Module 4: LIDAR Sensing

4 個視頻 （總計 48 分鐘）, 3 個閱讀材料, 1 個測驗
4 個視頻
Lesson 2: LIDAR Sensor Models and Point Clouds12分鐘
Lesson 3: Pose Estimation from LIDAR Data17分鐘
Optimizing State Estimation3分鐘
3 個閱讀材料
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10分鐘
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10分鐘
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30分鐘
1 個練習

## 關於 自动驾驶汽车 專項課程

Be at the forefront of the autonomous driving industry. With market researchers predicting a \$42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....

## 常見問題

• 讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：

• 课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
• 课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
• 您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。

• 如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策

• 是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多