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Overview
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 State Estimation and Localization for Self-Driving Cars offered by Coursera in partnership with University of Toronto 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.
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Visit programme websiteProgramme Structure
Courses include:
- Welcome to Course: State Estimation and Localization for Self-Driving Cars
- Least Squares
- State Estimation - Linear and Nonlinear Kalman Filters
- GNSS/INS Sensing for Pose Estimation
- LIDAR Sensing
- Putting It together - An Autonomous Vehicle State Estimator
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 2 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Disciplines
Geographical Information Systems (GIS) Automotive Engineering View 18 other Short Courses in Automotive Engineering in United StatesExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Advanced Level
- This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.
Make sure you meet all requirements
Visit programme websiteTuition Fee
To always see correct tuition fees
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 days. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 days.
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Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.
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