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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Coursera offers the Estimation and Localization for Self-Driving Cars certification course in association with the University of Toronto. The certification course introduces students to different sensors for correctly estimating the state and localization of a self-driving vehicle.
Moreover, by the end of the Estimation and Localization for Self-Driving Cars course, you will learn all about Kalman Filters and Iterative Closest Point algorithm with LIDAR. The certification course will also cover least squares, and how to relate GPS with IMUs. You will be able to build models for typical vehicle localization sensors.
Upon course completion, you will become adept in developing a full vehicle state estimator independently. The course material is comprehensive and features quizzes, as well as projects, to assist you in learning with ease.
Lastly, candidates will undertake a final project to complete the Coursera State Estimation and Localization for Self-Driving Cars course. On successful completion, candidates will receive a certificate from the University of Toronto, sharable on LinkedIn profiles or CV.
The Highlights
- Approx. 26 hours to complete
- Flexible deadlines
- Shareable certificate
- Self-paced virtual coaching
- Graded quizzes
- Subtitles available in Spanish, English, Russian, Portuguese (Brazilian), and French
- Graded feedback
- Practice exercises
- Readings
Programme Offerings
- Specialised assignments
- Graded Quizzes
- Feedback Team
- practice quizzes
- A/V classes
- Course Readings
- University of Toronto Certification
- Full Specialization
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
The State Estimation and Localization for Self-Driving Cars fees details:
Head | Amount |
1 Month | Rs. 6,757 |
3 Months | Rs. 13,514 |
6 Months | Rs. 20,271 |
Eligibility Criteria
To join the Coursera State Estimation and Localization for Self-Driving Cars course, candidates must have programming experience with Python 3.0. Additionally, you should have an advanced understanding of Physics, Calculus, Linear Algebra, and Statistics to derive maximum benefit from this certification programme.
What you will learn
State Estimation and Localization for Self-Driving Cars programme, will expose you to a host of critical information. After course completion, you should be able to perform the following:
- Use the CARLA simulator
- Register LIDAR point clouds
- Develop and test LIDAR sensor models
- Use Linear and Non-linear algorithms
- Understand the Iterative Closest Point algorithm
- Estimate vehicular state and road position
- Deploy least squares
- Relate Parameter with state estimation
- Develop models using GPS and IMUs
- Differentiate between different Kalman Filters
- Convert to Recursive form
Who it is for
The State Estimation and Localization Accreditation online course is primarily suitable for professionals from the engineering background. The certification course will prove beneficial for professionals such as:
- Robotics engineers
- Electrical engineers
- Computer engineers
- Mechanical engineers
Admission Details
You can register for the State Estimation and Localization for Self-Driving Cars online course using the following steps:
Step 1. Visit the course page.
Step 2. Click the ‘Enroll for Free’ button.
Step 3. Sign up Coursera using either your email ID or Google account.
Step 4. Finally, choose a course mode and pay the fee accordingly.
Application Details
No separate application form is needed for the State Estimation and Localization for Self-Driving Cars programme. You can simply sign-up for free using your Apple, Google, or Facebook account. You can also sign-up with your email address and access the course content instantly.
The Syllabus
Videos
- Welcome to the Self-Driving Cars Specialization!
- Welcome to the Course
- Meet the Instructor, Jonathan Kelly
- Meet the Instructor, Steven Waslander
- Meet Diana, Firmware Engineer
- Meet Winston, Software Engineer
- Meet Andy, Autonomous Systems Architect
- Meet Paul Newman, Founder, Oxbotica & Professor at the University of Oxford
- The Importance of State Estimation
Readings
- Course Prerequisites: Knowledge, Hardware & Software
- How to Use Discussion Forums
- How to Use Supplementary Readings in This Course
Discussion Prompt
- Get to Know Your Classmates
Videos
- Lesson 1 (Part 1): Squared Error Criterion and the Method of Least Squares
- Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares
- Lesson 2: Recursive Least Squares
- Lesson 3: Least Squares and the Method of Maximum Likelihood
Readings
- Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares
- Lesson 2 Supplementary Reading: Recursive Least Squares
- Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood
Assignments
- Lesson 1: Practice Quiz
- Lesson 2: Practice Quiz
- Module 1: Graded Quiz
Ungraded Labs
- Lesson 1 Practice Notebook: Least Squares
- Lesson 2 Practice Notebook: Recursive Least Squares
Videos
- Lesson 1: The (Linear) Kalman Filter
- Lesson 2: Kalman Filter and The Bias BLUEs
- Lesson 3: Going Nonlinear - The Extended Kalman Filter
- Lesson 4: An Improved EKF - The Error State Extended Kalman Filter
- Lesson 5: Limitations of the EKF
- Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter
Readings
- Lesson 1 Supplementary Reading: The Linear Kalman Filter
- Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs
- Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter
- Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman Filter
- Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter
Programming Assignment
- Module 2 Graded Notebook (Submission): Estimating a Vehicle Trajectory
Ungraded Lab
- Module 2 Graded Notebook: Estimating a Vehicle Trajectory
Videos
- Lesson 1: 3D Geometry and Reference Frames
- Lesson 2: The Inertial Measurement Unit (IMU)
- Lesson 3: The Global Navigation Satellite Systems (GNSS)
- Why Sensor Fusion?
Readings
- Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames
- Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)
- Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)
Assignment
- Module 3: Graded Quiz
Videos
- Lesson 1: Light Detection and Ranging Sensors
- Lesson 2: LIDAR Sensor Models and Point Clouds
- Lesson 3: Pose Estimation from LIDAR Data
- Optimizing State Estimation
Readings
- Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors
- Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds
- Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data
Assignment
- Module 4: Graded Quiz
Videos
- Lesson 1: State Estimation in Practice
- Lesson 2: Multisensor Fusion for State Estimation
- Lesson 3: Sensor Calibration - A Necessary Evil
- Lesson 4: Loss of One or More Sensors
- The Challenges of State Estimation
- Final Lesson: Project Overview
- Final Project Solution [LOCKED]
- Congratulations on Completing Course 2!
Readings
- Lesson 2 Supplementary Reading: Multisensor Fusion for State Estimation
- Lesson 3 Supplementary Reading: Sensor Calibration - A Neccessary Evil
Programming Assignment
- Final Project: Vehicle State Estimation on a Roadway
Discussion Prompt
- Your Learning Journey