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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31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4の2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31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

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

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
7時間で修了

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件のビデオ (合計54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 Filter10 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 FIlter
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

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件のビデオ (合計32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
2時間で修了

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
5
6時間で修了

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件のビデオ (合計50分), 2 readings, 1 quiz
8件のビデオ
Lesson 2: Multisensor Fusion for State Estimation8 分
Lesson 3: Sensor Calibration - A Necessary Evil9 分
Lesson 4: Loss of One or More Sensors5 分
The Challenges of State Estimation6 分
Final Lesson: Project Overview3 分
Final Project Solution [LOCKED]3 分
Congratulations on Completing Course 2!2 分
2件の学習用教材
Lesson 2 Supplementary Reading: Multisensor Fusion for State Estimation30 分
Lesson 3 Supplementary Reading: Sensor Calibration - A Neccessary Evil30 分
4.6
9件のレビューChevron Right

人気のレビュー

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 GHApr 29th 2019

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

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. ...

自動運転車の専門講座について

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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