About this Course
46,971 recent views

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

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

Approx. 25 hours to complete

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

English

Subtitles: English
User
Learners taking this Course are
  • Machine Learning Engineers
  • Data Scientists
  • Engineers
  • Researchers
  • Software Engineers

What you will learn

  • 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

User
Learners taking this Course are
  • Machine Learning Engineers
  • Data Scientists
  • Engineers
  • Researchers
  • Software Engineers

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

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

Approx. 25 hours to complete

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

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

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

9 videos (Total 33 min), 3 readings
9 videos
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
3 readings
Course Prerequisites: Knowledge, Hardware & Software15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
7 hours to complete

Module 1: Least Squares

4 videos (Total 33 min), 3 readings, 3 quizzes
4 videos
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
3 readings
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
3 practice exercises
Lesson 1: Practice Quiz30m
Lesson 2: Practice Quiz30m
Module 1: Graded Quiz50m
Week
2
7 hours to complete

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

6 videos (Total 53 min), 5 readings, 1 quiz
6 videos
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
5 readings
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
Week
3
2 hours to complete

Module 3: GNSS/INS Sensing for Pose Estimation

4 videos (Total 34 min), 3 readings, 1 quiz
4 videos
Lesson 2: The Inertial Measurement Unit (IMU)10m
Lesson 3: The Global Navigation Satellite Systems (GNSS)8m
Why Sensor Fusion?3m
3 readings
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
1 practice exercise
Module 3: Graded Quiz50m
Week
4
2 hours to complete

Module 4: LIDAR Sensing

4 videos (Total 48 min), 3 readings, 1 quiz
4 videos
Lesson 2: LIDAR Sensor Models and Point Clouds12m
Lesson 3: Pose Estimation from LIDAR Data17m
Optimizing State Estimation3m
3 readings
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
1 practice exercise
Module 4: Graded Quiz30m
4.6
25 ReviewsChevron Right

Top reviews from State Estimation and Localization for Self-Driving Cars

By 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.

By MIAug 12th 2019

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

Instructors

Avatar

Jonathan Kelly

Assistant Professor
Aerospace Studies
Avatar

Steven Waslander

Associate Professor
Aerospace Studies

About University of 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. ...

About the Self-Driving Cars Specialization

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)....
Self-Driving Cars

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.