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第 2 门课程(共 4 门)
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高级

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

完成时间大约为27 小时
英语(English)
字幕:英语(English)

您将学到的内容有

  • 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

可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
高级

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

完成时间大约为27 小时
英语(English)
字幕:英语(English)

提供方

多伦多大学 徽标

多伦多大学

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up94%(1,223 个评分)Info
1

1

完成时间为 2 小时

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

完成时间为 2 小时
9 个视频 (总计 33 分钟), 3 个阅读材料
9 个视频
Welcome to the Course3分钟
Meet the Instructor, Jonathan Kelly2分钟
Meet the Instructor, Steven Waslander5分钟
Meet Diana, Firmware Engineer2分钟
Meet Winston, Software Engineer3分钟
Meet Andy, Autonomous Systems Architect2分钟
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5分钟
The Importance of State Estimation1分钟
3 个阅读材料
Course Prerequisites: Knowledge, Hardware & Software15分钟
How to Use Discussion Forums15分钟
How to Use Supplementary Readings in This Course15分钟
完成时间为 7 小时

Module 1: Least Squares

完成时间为 7 小时
4 个视频 (总计 33 分钟), 3 个阅读材料, 3 个测验
4 个视频
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6分钟
Lesson 2: Recursive Least Squares7分钟
Lesson 3: Least Squares and the Method of Maximum Likelihood8分钟
3 个阅读材料
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45分钟
Lesson 2 Supplementary Reading: Recursive Least Squares30分钟
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30分钟
3 个练习
Lesson 1: Practice Quiz30分钟
Lesson 2: Practice Quiz30分钟
Module 1: Graded Quiz50分钟
2

2

完成时间为 7 小时

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

完成时间为 7 小时
6 个视频 (总计 53 分钟), 5 个阅读材料, 1 个测验
6 个视频
Lesson 2: Kalman Filter and The Bias BLUEs5分钟
Lesson 3: Going Nonlinear - The Extended Kalman Filter9分钟
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6分钟
Lesson 5: Limitations of the EKF7分钟
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15分钟
5 个阅读材料
Lesson 1 Supplementary Reading: The Linear Kalman Filter45分钟
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10分钟
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45分钟
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter1小时
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30分钟
3

3

完成时间为 2 小时

Module 3: GNSS/INS Sensing for Pose Estimation

完成时间为 2 小时
4 个视频 (总计 34 分钟), 3 个阅读材料, 1 个测验
4 个视频
Lesson 2: The Inertial Measurement Unit (IMU)10分钟
Lesson 3: The Global Navigation Satellite Systems (GNSS)8分钟
Why Sensor Fusion?3分钟
3 个阅读材料
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10分钟
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30分钟
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15分钟
1 个练习
Module 3: Graded Quiz50分钟
4

4

完成时间为 2 小时

Module 4: LIDAR Sensing

完成时间为 2 小时
4 个视频 (总计 48 分钟), 3 个阅读材料, 1 个测验
4 个视频
Lesson 2: LIDAR Sensor Models and Point Clouds12分钟
Lesson 3: Pose Estimation from LIDAR Data17分钟
Optimizing State Estimation3分钟
3 个阅读材料
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10分钟
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10分钟
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30分钟
1 个练习
Module 4: Graded Quiz30分钟

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关于 自动驾驶汽车 专项课程

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)....
自动驾驶汽车

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