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. 27 horas para completar
Inglês
Legendas: Francês, Português (Brasil), Russo, Inglês, Espanhol
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
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. 27 horas para completar
Inglês
Legendas: Francês, Português (Brasil), Russo, Inglês, Espanhol

oferecido por

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Universidade de Toronto

Programa - O que você aprenderá com este curso

Classificação do conteúdoThumbs Up95%(1,452 classificações)Info
Semana
1

Semana 1

2 horas para concluir

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

2 horas para concluir
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

7 horas para concluir
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

Semana 2

7 horas para concluir

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

7 horas para concluir
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

Semana 3

2 horas para concluir

Module 3: GNSS/INS Sensing for Pose Estimation

2 horas para concluir
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 exercício prático
Module 3: Graded Quiz50min
Semana
4

Semana 4

2 horas para concluir

Module 4: LIDAR Sensing

2 horas para concluir
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 exercício prático
Module 4: Graded Quiz30min

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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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