Об этом курсе
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Продвинутый уровень

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

Прибл. 31 час на выполнение

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

Английский

Субтитры: Английский

Чему вы научитесь

  • 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

100% онлайн

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Продвинутый уровень

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

Прибл. 31 час на выполнение

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

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
2 ч. на завершение

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....
9 видео ((всего 33 мин.)), 3 материалов для самостоятельного изучения
9 видео
Welcome to the Course3мин
Meet the Instructor, Jonathan Kelly2мин
Meet the Instructor, Steven Waslander5мин
Meet Diana, Firmware Engineer2мин
Meet Winston, Software Engineer3мин
Meet Andy, Autonomous Systems Architect2мин
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5мин
The Importance of State Estimation1мин
3 материала для самостоятельного изучения
Course Prerequisites: Knowledge, Hardware & Software15мин
How to Use Discussion Forums15мин
How to Use Supplementary Readings in This Course15мин
7 ч. на завершение

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....
4 видео ((всего 33 мин.)), 3 материалов для самостоятельного изучения, 3 тестов
4 видео
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6мин
Lesson 2: Recursive Least Squares7мин
Lesson 3: Least Squares and the Method of Maximum Likelihood8мин
3 материала для самостоятельного изучения
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45мин
Lesson 2 Supplementary Reading: Recursive Least Squares30мин
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30мин
3 практических упражнения
Lesson 1: Practice Quiz30мин
Lesson 2: Practice Quiz30мин
Module 1: Graded Quiz50мин
Неделя
2
7 ч. на завершение

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....
6 видео ((всего 54 мин.)), 5 материалов для самостоятельного изучения, 1 тест
6 видео
Lesson 2: Kalman Filter and The Bias BLUEs5мин
Lesson 3: Going Nonlinear - The Extended Kalman Filter10мин
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6мин
Lesson 5: Limitations of the EKF7мин
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15мин
5 материалов для самостоятельного изучения
Lesson 1 Supplementary Reading: The Linear Kalman Filter45мин
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10мин
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45мин
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30мин
Неделя
3
2 ч. на завершение

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....
4 видео ((всего 32 мин.)), 3 материалов для самостоятельного изучения, 1 тест
4 видео
Lesson 2: The Inertial Measurement Unit (IMU)10мин
Lesson 3: The Global Navigation Satellite Systems (GNSS)8мин
Why Sensor Fusion?3мин
3 материала для самостоятельного изучения
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10мин
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30мин
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15мин
1 практическое упражнение
Module 3: Graded Quiz50мин
Неделя
4
2 ч. на завершение

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)....
4 видео ((всего 48 мин.)), 3 материалов для самостоятельного изучения, 1 тест
4 видео
Lesson 2: LIDAR Sensor Models and Point Clouds12мин
Lesson 3: Pose Estimation from LIDAR Data17мин
Optimizing State Estimation3мин
3 материала для самостоятельного изучения
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10мин
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10мин
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30мин
1 практическое упражнение
Module 4: Graded Quiz30мин
Неделя
5
6 ч. на завершение

Module 5: Putting It together - An Autonomous Vehicle State Estimator

This module combines materials from Modules 1-4 together, with the goal of developing a full vehicle state estimator. Learners will build, using data from the CARLA simulator, an error-state extended Kalman filter-based estimator that incorporates GPS, IMU, and LIDAR measurements to determine the vehicle position and orientation on the road at a high update rate. There will be an opportunity to observe what happens to the quality of the state estimate when one or more of the sensors either 'drop out' or are disabled....
8 видео ((всего 50 мин.)), 2 материалов для самостоятельного изучения, 1 тест
8 видео
Lesson 2: Multisensor Fusion for State Estimation8мин
Lesson 3: Sensor Calibration - A Necessary Evil9мин
Lesson 4: Loss of One or More Sensors5мин
The Challenges of State Estimation6мин
Final Lesson: Project Overview3мин
Final Project Solution [LOCKED]3мин
Congratulations on Completing Course 2!2мин
2 материала для самостоятельного изучения
Lesson 2 Supplementary Reading: Multisensor Fusion for State Estimation30мин
Lesson 3 Supplementary Reading: Sensor Calibration - A Neccessary Evil30мин
4.6
Рецензии: 11Chevron Right

Лучшие рецензии

автор: 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.

автор: GHApr 29th 2019

one of best experiences. But the course requires a steep learning curve. The discussion forums are really helpful

Преподаватели

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Jonathan Kelly

Assistant Professor
Aerospace Studies
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Steven Waslander

Associate Professor
Aerospace Studies

О Торонтский университет

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. ...

О специализации ''Беспилотные автомобили'

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)....
Беспилотные автомобили

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