本課程是 Self-Driving Cars 專項課程 專項課程的一部分

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Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course.
This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. By the end of this course, you will be able to:
- 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
- Understand LIDAR scan matching and the Iterative Closest Point algorithm
- Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car
For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator.
This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws).

立即開始，按照自己的計劃學習。

根據您的日程表重置截止日期。

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

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

字幕：英語（English）

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

立即開始，按照自己的計劃學習。

根據您的日程表重置截止日期。

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

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

字幕：英語（English）

週

1This 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 個視頻 （總計 33 分鐘）, 3 個閱讀材料

Welcome to the Course3分鐘

Meet the Instructor, Jonathan Kelly2分鐘

Meet the Instructor, Steven Waslander5分鐘

Meet Diana, Firmware Engineer2分鐘

Meet Winston, Software Engineer3分鐘

Meet Andy, Autonomous Systems Architect2分鐘

Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5分鐘

The Importance of State Estimation1分鐘

Course Prerequisites: Knowledge, Hardware & Software15分鐘

How to Use Discussion Forums15分鐘

How to Use Supplementary Readings in This Course15分鐘

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 個視頻 （總計 33 分鐘）, 3 個閱讀材料, 3 個測驗

Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6分鐘

Lesson 2: Recursive Least Squares7分鐘

Lesson 3: Least Squares and the Method of Maximum Likelihood8分鐘

Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45分鐘

Lesson 2 Supplementary Reading: Recursive Least Squares30分鐘

Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30分鐘

Lesson 1: Practice Quiz30分鐘

Lesson 2: Practice Quiz30分鐘

Module 1: Graded Quiz50分鐘

週

2Any 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 個視頻 （總計 54 分鐘）, 5 個閱讀材料, 1 個測驗

Lesson 2: Kalman Filter and The Bias BLUEs5分鐘

Lesson 3: Going Nonlinear - The Extended Kalman Filter10分鐘

Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6分鐘

Lesson 5: Limitations of the EKF7分鐘

Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15分鐘

Lesson 1 Supplementary Reading: The Linear Kalman Filter45分鐘

Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10分鐘

Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45分鐘

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分鐘

週

3To 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 個視頻 （總計 32 分鐘）, 3 個閱讀材料, 1 個測驗

Lesson 2: The Inertial Measurement Unit (IMU)10分鐘

Lesson 3: The Global Navigation Satellite Systems (GNSS)8分鐘

Why Sensor Fusion?3分鐘

Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10分鐘

Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30分鐘

Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15分鐘

Module 3: Graded Quiz50分鐘

週

4LIDAR (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 個視頻 （總計 48 分鐘）, 3 個閱讀材料, 1 個測驗

Lesson 2: LIDAR Sensor Models and Point Clouds12分鐘

Lesson 3: Pose Estimation from LIDAR Data17分鐘

Optimizing State Estimation3分鐘

Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10分鐘

Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10分鐘

Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30分鐘

Module 4: Graded Quiz30分鐘

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.
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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)....

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