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State Estimation and Localization for Self-Driving Cars

Master advanced state estimation techniques and sensor fusion algorithms for precise autonomous vehicle localization and navigation system development.

Master advanced state estimation techniques and sensor fusion algorithms for precise autonomous vehicle localization and navigation system development.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Self-Driving Cars Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.7

(822 ratings)

51,370 already enrolled

English

پښتو, বাংলা, اردو, 3 more

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State Estimation and Localization for Self-Driving Cars

This course includes

26 Hours

Of Self-paced video lessons

Advanced Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Implement parameter and state estimation methods

  • Develop models for GPS and IMU sensors

  • Apply extended and unscented Kalman Filters

  • Master LIDAR scan matching techniques

  • Create multi-sensor fusion systems

  • Build complete vehicle state estimators

Skills you'll gain

Kalman Filters
State Estimation
LIDAR
GPS
IMU
Sensor Fusion
Vehicle Localization
Error-State EKF
Point Cloud Processing
Position Tracking

This course includes:

3.7 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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There are 6 modules in this course

This comprehensive course explores advanced state estimation and localization techniques for autonomous vehicles. Students learn essential methods including least squares estimation, Kalman filtering, and sensor fusion. The curriculum covers GPS/INS integration, LIDAR processing, and multi-sensor fusion strategies. Through hands-on projects using the CARLA simulator, participants implement an Error-State Extended Kalman Filter for vehicle localization.

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

Module 1 · 1 Hours to complete

Least Squares

Module 2 · 7 Hours to complete

State Estimation - Linear and Nonlinear Kalman Filters

Module 3 · 7 Hours to complete

GNSS/INS Sensing for Pose Estimation

Module 4 · 2 Hours to complete

LIDAR Sensing

Module 5 · 2 Hours to complete

Putting It together - An Autonomous Vehicle State Estimator

Module 6 · 6 Hours to complete

Fee Structure

Instructors

Jonathan Kelly
Jonathan Kelly

4.8 rating

650 Reviews

1,66,248 Students

4 Courses

Pioneering Autonomous Robotic Systems Expert

Dr. Jonathan Kelly is the Dean’s Catalyst Professor at the University of Toronto Institute for Aerospace Studies (UTIAS) and leads the Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory. His expertise spans developing advanced robotic systems that can fly, drive, swim, and grasp. Dr. Kelly completed his Ph.D. at the University of Southern California, focusing on sensor fusion for robust robot navigation, followed by postdoctoral research at the Massachusetts Institute of Technology. Before academia, he worked as a software engineer at the Canadian Space Agency, contributing to space technology development.

Steven Waslander
Steven Waslander

4.8 rating

468 Reviews

1,68,651 Students

4 Courses

Associate Professor

Prof. Steven Waslander is a leading authority on autonomous aerial and ground vehicles, including multirotor drones and autonomous driving, Simultaneous Localization and Mapping (SLAM) and multi-vehicle systems. He received his B.Sc.E.in 1998 from Queen’s University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics, where as a graduate student he created the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), the world’s most capable outdoor multi-vehicle quadrotor platform at the time. He was recruited to Waterloo from Stanford in 2008, where he founded and directs the Waterloo Autonomous Vehicle Laboratory (WAVELab), extending the state of the art in autonomous drones and autonomous driving through advances in localization and mapping, object detection and tracking, integrated planning and control methods and multi-robot coordination. In 2018, he joined the University of Toronto Institute for Aerospace Studies (UTIAS), and founded the Toronto Robotics and Artificial Intelligence Laboratory (TRAILab). Prof. Waslander’s innovations in drone research were recognized by the Ontario Centres of Excellence Mind to Market award for the best Industry/Academia collaboration (2012, with Aeryon Labs), best paper and best poster awards at the Computer and Robot Vision Conference (2018), and through two Outstanding Performance Awards, and two Distinguished Performance Awards while at the University of Waterloo. His work on autonomous vehicles has resulted in the Autonomoose, the first autonomous vehicle created at a Canadian University to drive over 100 km on public roads. His insights into autonomous driving have been featured in the Globe and Mail, Toronto Star, National Post, the Rick Mercer Report, and on national CBC Radio. He is Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems, has served as the General Chair for the International Autonomous Robot Racing Competition (IARRC 2012-15), as the program chair for the 13th and 14th Conference on Computer and Robot Vision (CRV 2016-17), and as the Competitions Chair for the International Conference on Intelligent Robots and Systems (IROS 2017).

State Estimation and Localization for Self-Driving Cars

This course includes

26 Hours

Of Self-paced video lessons

Advanced Level

Completion Certificate

awarded on course completion

Free course

Testimonials

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

4.7 course rating

822 ratings

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.