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第 2 門課程(共 4 門)
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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27 小時
英語(English)
字幕:英語(English)

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

可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 2 門課程(共 4 門)
可靈活調整截止日期
根據您的日程表重置截止日期。
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27 小時
英語(English)
字幕:英語(English)

提供方

多伦多大学 徽標

多伦多大学

教學大綱 - 您將從這門課程中學到什麼

內容評分Thumbs Up94%(1,046 個評分)Info
1

1

完成時間為 2 小時

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

完成時間為 2 小時
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
完成時間為 7 小時

Module 1: Least Squares

完成時間為 7 小時
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
Module 1: Graded Quiz50分鐘
2

2

完成時間為 7 小時

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

完成時間為 7 小時
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

3

完成時間為 2 小時

Module 3: GNSS/INS Sensing for Pose Estimation

完成時間為 2 小時
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
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15分鐘
1 個練習
Module 3: Graded Quiz50分鐘
4

4

完成時間為 2 小時

Module 4: LIDAR Sensing

完成時間為 2 小時
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
Module 4: Graded Quiz30分鐘

審閱

來自STATE ESTIMATION AND LOCALIZATION FOR SELF-DRIVING CARS的熱門評論

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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)....
自动驾驶汽车

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