このコースについて

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100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の2コース
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上級レベル

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

約27時間で修了
英語
字幕:英語

学習内容

  • 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

共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の2コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル

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

約27時間で修了
英語
字幕:英語

提供:

トロント大学(University of Toronto) ロゴ

トロント大学(University of Toronto)

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up94%(1,054 件の評価)Info
1

1

2時間で修了

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

2時間で修了
9件のビデオ (合計33分), 3 readings
9件のビデオ
Welcome to the Course3 分
Meet the Instructor, Jonathan Kelly2 分
Meet the Instructor, Steven Waslander5 分
Meet Diana, Firmware Engineer2 分
Meet Winston, Software Engineer3 分
Meet Andy, Autonomous Systems Architect2 分
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5 分
The Importance of State Estimation1 分
3件の学習用教材
Course Prerequisites: Knowledge, Hardware & Software15 分
How to Use Discussion Forums15 分
How to Use Supplementary Readings in This Course15 分
7時間で修了

Module 1: Least Squares

7時間で修了
4件のビデオ (合計33分), 3 readings, 3 quizzes
4件のビデオ
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6 分
Lesson 2: Recursive Least Squares7 分
Lesson 3: Least Squares and the Method of Maximum Likelihood8 分
3件の学習用教材
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45 分
Lesson 2 Supplementary Reading: Recursive Least Squares30 分
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30 分
3の練習問題
Lesson 1: Practice Quiz30 分
Lesson 2: Practice Quiz30 分
Module 1: Graded Quiz50 分
2

2

7時間で修了

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

7時間で修了
6件のビデオ (合計53分), 5 readings, 1 quiz
6件のビデオ
Lesson 2: Kalman Filter and The Bias BLUEs5 分
Lesson 3: Going Nonlinear - The Extended Kalman Filter9 分
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6 分
Lesson 5: Limitations of the EKF7 分
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15 分
5件の学習用教材
Lesson 1 Supplementary Reading: The Linear Kalman Filter45 分
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10 分
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45 分
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter1 時間
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30 分
3

3

2時間で修了

Module 3: GNSS/INS Sensing for Pose Estimation

2時間で修了
4件のビデオ (合計34分), 3 readings, 1 quiz
4件のビデオ
Lesson 2: The Inertial Measurement Unit (IMU)10 分
Lesson 3: The Global Navigation Satellite Systems (GNSS)8 分
Why Sensor Fusion?3 分
3件の学習用教材
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10 分
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30 分
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15 分
1の練習問題
Module 3: Graded Quiz50 分
4

4

2時間で修了

Module 4: LIDAR Sensing

2時間で修了
4件のビデオ (合計48分), 3 readings, 1 quiz
4件のビデオ
Lesson 2: LIDAR Sensor Models and Point Clouds12 分
Lesson 3: Pose Estimation from LIDAR Data17 分
Optimizing State Estimation3 分
3件の学習用教材
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10 分
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10 分
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30 分
1の練習問題
Module 4: Graded Quiz30 分

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自動運転車専門講座について

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)....
自動運転車

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