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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26時間で修了

推奨:4 weeks of study, 5-6 hours per week...

英語

字幕:英語

学習内容

  • Check

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

  • Check

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

  • Check

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

  • Check

    Apply LIDAR scan matching and the Iterative Closest Point algorithm

次における1の1コース

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。

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26時間で修了

推奨:4 weeks of study, 5-6 hours per week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
2時間で修了

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

9件のビデオ (合計33分), 3 readings
9件のビデオ
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

4件のビデオ (合計33分), 3 readings, 3 quizzes
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
7時間で修了

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

6件のビデオ (合計54分), 5 readings, 1 quiz
6件のビデオ
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
2時間で修了

Module 3: GNSS/INS Sensing for Pose Estimation

4件のビデオ (合計34分), 3 readings, 1 quiz
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
2時間で修了

Module 4: LIDAR Sensing

4件のビデオ (合計48分), 3 readings, 1 quiz
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
4.6
25件のレビューChevron Right

State Estimation and Localization for Self-Driving Cars からの人気レビュー

by RLApr 27th 2019

It provides a hand-on experience in implementing part of the localization process...interesting stuff!! Kind of time-consuming so be prepared.

by MIAug 12th 2019

Very interesting course if you want to learn about the different filters used in self driving cars for sensor fusion

講師

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Jonathan Kelly

Assistant Professor
Aerospace Studies
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Steven Waslander

Associate Professor
Aerospace Studies

トロント大学(University of Toronto)について

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

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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