Informações sobre o curso
4.6
32 classificações
5 avaliações

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Comece imediatamente e aprenda em seu próprio cronograma.

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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. 32 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. 32 horas para completar

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

Inglês

Legendas: Inglês

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

This 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 vídeos (total de (Total 33 mín.) min), 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

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 vídeos (total de (Total 33 mín.) min), 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

Any 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 vídeos (total de (Total 54 mín.) min), 5 leituras, 1 teste
6 videos
Lesson 2: Kalman Filter and The Bias BLUEs5min
Lesson 3: Going Nonlinear - The Extended Kalman Filter10min
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 FIlters
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

To 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 vídeos (total de (Total 32 mín.) min), 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
2 horas para concluir

Module 4: LIDAR Sensing

LIDAR (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 vídeos (total de (Total 48 mín.) min), 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
4.6
5 avaliaçõesChevron Right

Melhores avaliações

por LKMar 1st 2019

Sometimes hard, but still pretty much fun to solve all the problems :)

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 o 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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