课程信息
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专项课程

第 2 门课程(共 4 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

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(小时)

完成时间大约为16 小时

建议:4 weeks of study, 5-6 hours per week...
可选语言

英语(English)

字幕:英语(English)

您将学到的内容有

  • 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

专项课程

第 2 门课程(共 4 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

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(小时)

完成时间大约为16 小时

建议:4 weeks of study, 5-6 hours per week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 2 小时

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

This module introduces you to the main concepts discussed in the course and presents the layout of the course. The module describes and motivates the problems of state estimation and localization for self-driving cars....
Reading
9 个视频 (总计 33 分钟), 3 个阅读材料
Video9 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
Reading3 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(小时)
完成时间为 7 小时

Module 1: Least Squares

The method of least squares, developed by Carl Friedrich Gauss in 1795, is a well known technique for estimating parameter values from data. This module provides a review of least squares, for the cases of unweighted and weighted observations. There is a deep connection between least squares and maximum likelihood estimators (when the observations are considered to be Gaussian random variables) and this connection is established and explained. Finally, the module develops a technique to transform the traditional 'batch' least squares estimator to a recursive form, suitable for online, real-time estimation applications....
Reading
4 个视频 (总计 33 分钟), 3 个阅读材料, 3 个测验
Video4 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
Reading3 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
Quiz3 个练习
Lesson 1: Practice Quiz30分钟
Lesson 2: Practice Quiz30分钟
Module 1: Graded Quiz50分钟
2
完成时间(小时)
完成时间为 7 小时

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

Any engineer working on autonomous vehicles must understand the Kalman filter, first described in a paper by Rudolf Kalman in 1960. The filter has been recognized as one of the top 10 algorithms of the 20th century, is implemented in software that runs on your smartphone and on modern jet aircraft, and was crucial to enabling the Apollo spacecraft to reach the moon. This module derives the Kalman filter equations from a least squares perspective, for linear systems. The module also examines why the Kalman filter is the best linear unbiased estimator (that is, it is optimal in the linear case). The Kalman filter, as originally published, is a linear algorithm; however, all systems in practice are nonlinear to some degree. Shortly after the Kalman filter was developed, it was extended to nonlinear systems, resulting in an algorithm now called the ‘extended’ Kalman filter, or EKF. The EKF is the ‘bread and butter’ of state estimators, and should be in every engineer’s toolbox. This module explains how the EKF operates (i.e., through linearization) and discusses its relationship to the original Kalman filter. The module also provides an overview of the unscented Kalman filter, a more recently developed and very popular member of the Kalman filter family....
Reading
6 个视频 (总计 54 分钟), 5 个阅读材料, 1 个测验
Video6 个视频
Lesson 2: Kalman Filter and The Bias BLUEs5分钟
Lesson 3: Going Nonlinear - The Extended Kalman Filter10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
Reading5 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 FIlter
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30分钟
3
完成时间(小时)
完成时间为 2 小时

Module 3: GNSS/INS Sensing for Pose Estimation

To navigate reliably, autonomous vehicles require an estimate of their pose (position and orientation) in the world (and on the road) at all times. Much like for modern aircraft, this information can be derived from a combination of GPS measurements and inertial navigation system (INS) data. This module introduces sensor models for inertial measurement units and GPS (and, more broadly, GNSS) receivers; performance and noise characteristics are reviewed. The module describes ways in which the two sensor systems can be used in combination to provide accurate and robust vehicle pose estimates....
Reading
4 个视频 (总计 32 分钟), 3 个阅读材料, 1 个测验
Video4 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
Reading3 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
Quiz1 个练习
Module 3: Graded Quiz50分钟
4
完成时间(小时)
完成时间为 2 小时

Module 4: LIDAR Sensing

LIDAR (light detection and ranging) sensing is an enabling technology for self-driving vehicles. LIDAR sensors can ‘see’ farther than cameras and are able to provide accurate range information. This module develops a basic LIDAR sensor model and explores how LIDAR data can be used to produce point clouds (collections of 3D points in a specific reference frame). Learners will examine ways in which two LIDAR point clouds can be registered, or aligned, in order to determine how the pose of the vehicle has changed with time (i.e., the transformation between two local reference frames)....
Reading
4 个视频 (总计 48 分钟), 3 个阅读材料, 1 个测验
Video4 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
Reading3 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
Quiz1 个练习
Module 4: Graded Quiz30分钟

讲师

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Jonathan Kelly

Assistant Professor
Aerospace Studies
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Steven Waslander

Associate Professor
Aerospace Studies

关于 多伦多大学

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. ...

关于 Self-Driving Cars 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)....
Self-Driving Cars

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