About this Course
최근 조회 46,736

다음 전문 분야의 4개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

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

완료하는 데 약 25시간 필요

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

영어

자막: 영어

배울 내용

  • 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

다음 전문 분야의 4개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

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

완료하는 데 약 25시간 필요

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

영어

자막: 영어

강의 하이라이트

featured

산업 통찰력

자신의 경험을 공유하는 초빙 강사진

동영상 인터뷰를 통해 자동화 차량 기술을 연구하는 엔지니어들이 업계에서 앞서나가려면 어떤 기술이 필요한지에 대해 알려드립니다.

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

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

9개 동영상 (총 33분), 3 readings
9개의 동영상
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
3개의 읽기 자료
Course Prerequisites: Knowledge, Hardware & Software15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
완료하는 데 7시간 필요

Module 1: Least Squares

4개 동영상 (총 33분), 3 readings, 3 quizzes
4개의 동영상
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
3개의 읽기 자료
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
3개 연습문제
Lesson 1: Practice Quiz30m
Lesson 2: Practice Quiz30m
Module 1: Graded Quiz50m
2
완료하는 데 7시간 필요

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

6개 동영상 (총 53분), 5 readings, 1 quiz
6개의 동영상
Lesson 2: Kalman Filter and The Bias BLUEs5m
Lesson 3: Going Nonlinear - The Extended Kalman Filter9m
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
5개의 읽기 자료
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 FIlter1h
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30m
3
완료하는 데 2시간 필요

Module 3: GNSS/INS Sensing for Pose Estimation

4개 동영상 (총 34분), 3 readings, 1 quiz
4개의 동영상
Lesson 2: The Inertial Measurement Unit (IMU)10m
Lesson 3: The Global Navigation Satellite Systems (GNSS)8m
Why Sensor Fusion?3m
3개의 읽기 자료
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
1개 연습문제
Module 3: Graded Quiz50m
4
완료하는 데 2시간 필요

Module 4: LIDAR Sensing

4개 동영상 (총 48분), 3 readings, 1 quiz
4개의 동영상
Lesson 2: LIDAR Sensor Models and Point Clouds12m
Lesson 3: Pose Estimation from LIDAR Data17m
Optimizing State Estimation3m
3개의 읽기 자료
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
1개 연습문제
Module 4: Graded Quiz30m
4.6
25개의 리뷰Chevron Right

State Estimation and Localization for Self-Driving Cars의 최상위 리뷰

대학: 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.

대학: MIAug 12th 2019

Very interesting course if you want to learn about the different filters used in self driving cars for sensor fusion

강사

Avatar

Jonathan Kelly

Assistant Professor
Aerospace Studies
Avatar

Steven Waslander

Associate Professor
Aerospace Studies

토론토 대학교 정보

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. ...

자율 주행 자동차 전문 분야 정보

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)....
자율 주행 자동차

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.