Informações sobre o curso
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Nível avançado

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

Aprox. 25 horas para completar

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

Inglês

Legendas: Inglês

O que você vai aprender

  • 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% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

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

Aprox. 25 horas para completar

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

Inglês

Legendas: Inglês

Os alunos fazendo este Course são

  • Machine Learning Engineers
  • Data Scientists
  • Engineers
  • Researchers
  • Software Engineers

Programa - O que você aprenderá com este curso

Semana
1
2 horas para concluir

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

9 vídeos (Total 33 mín.), 3 leituras
9 videos
Welcome to the Course3min
Meet the Instructor, Jonathan Kelly2min
Meet the Instructor, Steven Waslander5min
Meet Diana, Firmware Engineer2min
Meet Winston, Software Engineer3min
Meet Andy, Autonomous Systems Architect2min
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5min
The Importance of State Estimation1min
3 leituras
Course Prerequisites: Knowledge, Hardware & Software15min
How to Use Discussion Forums15min
How to Use Supplementary Readings in This Course15min
7 horas para concluir

Module 1: Least Squares

4 vídeos (Total 33 mín.), 3 leituras, 3 testes
4 videos
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6min
Lesson 2: Recursive Least Squares7min
Lesson 3: Least Squares and the Method of Maximum Likelihood8min
3 leituras
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45min
Lesson 2 Supplementary Reading: Recursive Least Squares30min
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30min
3 exercícios práticos
Lesson 1: Practice Quiz30min
Lesson 2: Practice Quiz30min
Module 1: Graded Quiz50min
Semana
2
7 horas para concluir

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

6 vídeos (Total 53 mín.), 5 leituras, 1 teste
6 videos
Lesson 2: Kalman Filter and The Bias BLUEs5min
Lesson 3: Going Nonlinear - The Extended Kalman Filter9min
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6min
Lesson 5: Limitations of the EKF7min
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15min
5 leituras
Lesson 1 Supplementary Reading: The Linear Kalman Filter45min
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10min
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45min
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter1h
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30min
Semana
3
2 horas para concluir

Module 3: GNSS/INS Sensing for Pose Estimation

4 vídeos (Total 34 mín.), 3 leituras, 1 teste
4 videos
Lesson 2: The Inertial Measurement Unit (IMU)10min
Lesson 3: The Global Navigation Satellite Systems (GNSS)8min
Why Sensor Fusion?3min
3 leituras
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10min
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30min
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15min
1 exercícios práticos
Module 3: Graded Quiz50min
Semana
4
2 horas para concluir

Module 4: LIDAR Sensing

4 vídeos (Total 48 mín.), 3 leituras, 1 teste
4 videos
Lesson 2: LIDAR Sensor Models and Point Clouds12min
Lesson 3: Pose Estimation from LIDAR Data17min
Optimizing State Estimation3min
3 leituras
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10min
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10min
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30min
1 exercícios práticos
Module 4: Graded Quiz30min
4.6
25 avaliaçõesChevron Right

Principais avaliações do State Estimation and Localization for Self-Driving Cars

por 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.

por MIAug 12th 2019

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

Instrutores

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Jonathan Kelly

Assistant Professor
Aerospace Studies
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Steven Waslander

Associate Professor
Aerospace Studies

Sobre Universidade de Toronto

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. ...

Sobre Programa de cursos integrados Carros autoguiáveis

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)....
Carros autoguiáveis

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  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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