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#### 100% online

Start instantly and learn at your own schedule.

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

#### Approx. 26 hours to complete

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

#### English

Subtitles: English

### What you will learn

• Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares

• Develop a model for typical vehicle localization sensors, including GPS and IMUs

• Apply extended and unscented Kalman Filters to a vehicle state estimation problem

• Apply LIDAR scan matching and the Iterative Closest Point algorithm

#### 100% online

Start instantly and learn at your own schedule.

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

#### Approx. 26 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
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
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 3: Least Squares and the Method of Maximum Likelihood8m
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
Week
2
7 hours to complete

## Module 2: State Estimation - Linear and Nonlinear Kalman Filters

6 videos (Total 54 min), 5 readings, 1 quiz
6 videos
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
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
Why Sensor Fusion?3m
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10m
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30m
1 practice exercise
Week
4
2 hours to complete

## Module 4: LIDAR Sensing

4 videos (Total 48 min), 3 readings, 1 quiz
4 videos
Optimizing State Estimation3m
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
4.6
25 Reviews

### 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

### Jonathan Kelly

Assistant Professor
Aerospace Studies

### Steven Waslander

Associate Professor
Aerospace Studies

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)....