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자율 주행 자동차 전문 분야

토론토 대학교

About this Course

4.6

58개의 평가

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10개의 리뷰

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).

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

일정에 따라 마감일을 재설정합니다.

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

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

자막: 영어

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

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

일정에 따라 마감일을 재설정합니다.

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

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

자막: 영어

주

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 videos (Total 33 min), 3 readings

Welcome to the Course3m

Meet the Instructor, Jonathan Kelly2m

Meet the Instructor, Steven Waslander5m

Meet Diana, Firmware Engineer2m

Meet Winston, Software Engineer3m

Meet Andy, Autonomous Systems Architect2m

Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5m

The Importance of State Estimation1m

Course Prerequisites: Knowledge, Hardware & Software15m

How to Use Discussion Forums15m

How to Use Supplementary Readings in This Course15m

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 videos (Total 33 min), 3 readings, 3 quizzes

Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6m

Lesson 2: Recursive Least Squares7m

Lesson 3: Least Squares and the Method of Maximum Likelihood8m

Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45m

Lesson 2 Supplementary Reading: Recursive Least Squares30m

Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30m

Lesson 1: Practice Quiz30m

Lesson 2: Practice Quiz30m

Module 1: Graded Quiz50m

주

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 videos (Total 54 min), 5 readings, 1 quiz

Lesson 2: Kalman Filter and The Bias BLUEs5m

Lesson 3: Going Nonlinear - The Extended Kalman Filter10m

Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6m

Lesson 5: Limitations of the EKF7m

Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15m

Lesson 1 Supplementary Reading: The Linear Kalman Filter45m

Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10m

Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45m

Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter

Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30m

주

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 videos (Total 32 min), 3 readings, 1 quiz

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

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

Why Sensor Fusion?3m

Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10m

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

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

Module 3: Graded Quiz50m

주

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 videos (Total 48 min), 3 readings, 1 quiz

Lesson 2: LIDAR Sensor Models and Point Clouds12m

Lesson 3: Pose Estimation from LIDAR Data17m

Optimizing State Estimation3m

Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10m

Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10m

Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30m

Module 4: Graded Quiz30m

주

5This 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 videos (Total 50 min), 2 readings, 1 quiz

Lesson 2: Multisensor Fusion for State Estimation8m

Lesson 3: Sensor Calibration - A Necessary Evil9m

Lesson 4: Loss of One or More Sensors5m

The Challenges of State Estimation6m

Final Lesson: Project Overview3m

Final Project Solution [LOCKED]3m

Congratulations on Completing Course 2!2m

Lesson 2 Supplementary Reading: Multisensor Fusion for State Estimation30m

Lesson 3 Supplementary Reading: Sensor Calibration - A Neccessary Evil30m

4.6

10개의 리뷰대학: RL•Apr 27th 2019

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

대학: GH•Apr 29th 2019

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

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.
...

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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