State Estimation and Localization for Self-Driving Cars from Coursera

Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course.

This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. By the end of this course, you will be able to:

- 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

- Understand LIDAR scan matching and the Iterative Closest Point algorithm

- Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car

For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator.

This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws).

What's inside

Syllabus

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. An accurate estimate of the vehicle state and its position on the road is required at all times to drive safely.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a deep dive into state estimation and localization techniques critical for the development of self-driving cars

Taught by instructors Jonathan Kelly and Steven Waslander, renowned experts in robotics and autonomous vehicle research

Designed for learners with a background in engineering or robotics, catering to a specialized audience

Requires strong fundamentals in linear algebra, statistics, calculus, and physics

Includes a practical project involving the implementation of an Error-State Extended Kalman Filter (ES-EKF) in the CARLA simulator

Covers essential sensor models for GPS, IMUs, and LIDAR, ensuring a comprehensive understanding of sensor data

Reviews summary

Advanced localization for autonomous systems

According to learners, this course provides a solid foundation in state estimation and localization for self-driving cars. Many highlight the in-depth coverage of core concepts like Kalman filters (EKF/UKF) and least squares, essential for autonomous vehicle development. The practical project using the CARLA simulator is frequently praised for providing valuable hands-on experience, allowing students to implement key algorithms. While the course is described as mathematically rigorous and requires a strong background in prerequisites like linear algebra, statistics, and physics, those with the necessary foundation find it highly valuable. Some reviewers note the pace can be challenging, but overall, students feel they gain practical tools and knowledge applicable to real-world problems.

Directly applicable to self-driving cars.

"Excellent course for anyone looking to work in the autonomous vehicle industry."

"The knowledge gained here is directly relevant to real-world self-driving challenges."

"Highly recommend this course if you are serious about autonomous navigation."

Deep dive into essential algorithms.

"Great content on the underlying principles of state estimation."

"Provides a solid foundation in estimation algorithms, particularly Kalman Filters."

"The explanations of EKF and UKF were very thorough and easy to follow."

"I appreciated the detailed mathematical derivations for least squares and Kalman filters."

Hands-on implementation using a simulator.

"The final project in CARLA was incredibly useful for implementing the ES-EKF."

"Implementing the algorithms in CARLA simulator helped solidify my understanding immensely."

"Loved the practical assignment where we had to apply the filter to real-like data."

"Getting to work with sensor fusion in a simulated environment was a major plus."

Content is dense and moves quickly.

"The course covers a lot of complex material relatively quickly."

"Sometimes the pace felt a bit fast, especially with the mathematical concepts."

"Requires dedication and time to keep up with the lectures and assignments."

Requires strong math/physics background.

"You definitely need a strong grasp of Linear Algebra, Statistics, and Calculus to succeed."

"Be prepared for significant mathematical content; prerequisites are not just suggestions."

"I struggled initially because my linear algebra wasn't as strong as needed."

"This course is not for beginners; prior knowledge of the stated prerequisites is crucial."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in State Estimation and Localization for Self-Driving Cars with these activities:

Review previous coursework

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Review previously learned material in Linear Algebra, Statistics, Calculus and Physics to prepare for the course.

Browse courses on State Estimation

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Review lecture notes from previous courses
Go through practice problems and assignments
Take practice quizzes and tests

Form a study group with classmates

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Enhance learning by collaborating with peers through a study group, where you can discuss course material, share insights, and work through problems together.

Browse courses on Self-Driving Cars

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Find a group of classmates who are interested in forming a study group
Set up regular meeting times
Prepare for each meeting by reading the assigned material and completing any homework
Discuss the material, ask questions, and work through problems together

Gather resources on sensor fusion

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Compile a collection of resources (e.g., articles, videos, datasets) on sensor fusion, which will expand knowledge and understanding of how different sensors are combined for state estimation.

Browse courses on Sensor Fusion

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Search for relevant resources online
Organize the resources into a logical structure
Share the compilation with other students or online communities

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Attend a workshop on self-driving car technology

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Deepen understanding and gain insights into the latest advancements in self-driving car technology by attending a workshop led by experts in the field.

Browse courses on Self-Driving Cars

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Find relevant workshops and conferences
Register and attend the workshop
Take notes and actively participate in discussions

Review 'Autonomous Vehicle Technology' by James M. Anderson

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Gain a comprehensive overview of the field by reading 'Autonomous Vehicle Technology', which provides a thorough examination of the key concepts, technologies, and challenges in self-driving car development.

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Read the book thoroughly
Take notes and highlight important passages
Discuss the book's concepts with other students or experts

Follow tutorials on Kalman filters

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Reinforce understanding of Kalman filters by following online tutorials, which will provide step-by-step guidance and examples.

Browse courses on Kalman Filter

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Find online tutorials on the Kalman filter
Go through the tutorials at your own pace
Complete the exercises and practice problems

Practice least squares estimation

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Practice applying the method of least squares to estimate parameters in various scenarios, which will strengthen foundational skills in parameter estimation.

Browse courses on Least Squares

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Find practice problems and datasets online
Solve the problems using the least squares method
Compare your results to the expected values

Build a simple state estimator for a moving object

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Apply course concepts by building a simple state estimator for a moving object, which will provide practical experience in implementing and evaluating state estimation algorithms.

Browse courses on State Estimation

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Choose an appropriate state estimation algorithm
Define the state space model for the moving object
Implement the algorithm and test it using simulated data
Evaluate the performance of the estimator

Career center

Learners who complete State Estimation and Localization for Self-Driving Cars will develop knowledge and skills that may be useful to these careers:

Sensor Engineer

A Sensor Engineer is responsible for designing, developing, and testing sensors used in a variety of applications, including self-driving cars. This course would be particularly useful for Sensor Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing accurate and reliable sensors for self-driving cars.

See salaries and explore the career path for Sensor Engineer

Controls Engineer

A Controls Engineer is responsible for designing, developing, and testing control systems for a variety of applications, including self-driving cars. This course would be particularly useful for Controls Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing robust and reliable control systems for self-driving cars.

See salaries and explore the career path for Controls Engineer

Software Engineer

Software Engineers design, develop, and test software for a variety of applications, including self-driving cars. This course would be helpful for Software Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing robust and reliable software for self-driving cars.

See salaries and explore the career path for Software Engineer

Robotics Engineer

Robotics Engineers design, build, and maintain robots, which are becoming increasingly important in a variety of industries, including the automotive industry. This course would be helpful for Robotics Engineers who want to learn more about the specific challenges of state estimation and localization for self-driving cars. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing robots that can navigate safely and autonomously.

See salaries and explore the career path for Robotics Engineer

Systems Engineer

A Systems Engineer is responsible for designing, developing, and testing systems that integrate hardware and software, including self-driving cars. This course would be helpful for Systems Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing safe and reliable systems for self-driving cars.

See salaries and explore the career path for Systems Engineer

Data Scientist

Data Scientists use data to solve problems and make predictions, which is becoming increasingly important in a variety of industries, including the automotive industry. This course would be helpful for Data Scientists who want to learn more about the specific challenges of state estimation and localization for self-driving cars. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing data-driven solutions for self-driving cars.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and test AI systems, which are becoming increasingly important in a variety of industries, including the automotive industry. This course would be helpful for Artificial Intelligence Engineers who want to learn more about the specific challenges of state estimation and localization for self-driving cars. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing safe and reliable AI systems for self-driving cars.

See salaries and explore the career path for Artificial Intelligence Engineer

Automotive Engineer

Automotive Engineers design, develop, and test automobiles, including self-driving cars. This course would be helpful for Automotive Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing safe and reliable self-driving cars.

See salaries and explore the career path for Automotive Engineer

Electrical Engineer

Electrical Engineers design, develop, and test electrical systems, including those used in self-driving cars. This course would be helpful for Electrical Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing robust and reliable electrical systems for self-driving cars.

See salaries and explore the career path for Electrical Engineer

Mechanical Engineer

Mechanical Engineers design, develop, and test mechanical systems, including those used in self-driving cars. This course would be helpful for Mechanical Engineers who want to learn more about the specific challenges of state estimation and localization for autonomous vehicles. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing safe and reliable mechanical systems for self-driving cars.

See salaries and explore the career path for Mechanical Engineer

Project Manager

Project Managers plan, execute, and close projects, which is becoming increasingly important in a variety of industries, including the automotive industry. This course would be helpful for Project Managers who want to learn more about the specific challenges of state estimation and localization for self-driving cars. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing and managing successful self-driving car projects.

See salaries and explore the career path for Project Manager

Business Analyst

Business Analysts gather and analyze data to help businesses make better decisions, which is becoming increasingly important in a variety of industries, including the automotive industry. This course would be helpful for Business Analysts who want to learn more about the specific challenges of state estimation and localization for self-driving cars. The course covers topics such as Kalman filtering, LIDAR sensing, and GPS/INS sensing, which are all essential for developing data-driven solutions for self-driving cars.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers plan, develop, and launch products, which is becoming increasingly important in a variety of industries, including the automotive industry.

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Technical Writer

Technical Writers create documentation for a variety of products and services, including self-driving cars.

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Sales Engineer

Sales Engineers sell products and services to businesses, including self-driving cars.

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