Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure

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 is Rs. 6,486 per month
Initially, the programme is available to try free for 7 days.

The State Estimation and Localization for Self-Driving Cars fees details:

Head	Amount
1 Month	Rs. 6,486
3 Months	Rs. 12,973
6 Months	Rs. 19,459

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

Programming skills

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

Electrical EngineerRobotics EngineerComputer Systems EngineerMechanical Engineer

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

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

Practice Exercises

Lesson 1: Practice Quiz
Lesson 2: Practice Quiz
Module 1: Graded Quiz

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
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

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)

Practice Exercise

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

Practice Exercise

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

Instructors

University of Toronto, Toronto Frequently Asked Questions (FAQ's)

1: What is the importance of the State Estimation and Localization for Self-Driving Cars programme from Coursera?

The automobile industry is undergoing a silent revolution, as developers test out novel technology in the autonomous driving sector. Top market researchers predict exponential growth of this sector by the turn of this decade. Therefore, if you have the aptitude, you can sign-up and upskill to boost your career.

2: What are the prerequisites for this course?

Applicants must have advanced knowledge of Physics, Calculus, Linear Algebra, and Statistics. Additionally, they must have programming experience with Python 3.0.

3: How is this training programme structured?

The training module is completely virtual, with flexible deadlines. Candidates take typically Approx 26 hours to complete the programme. While the lessons are in English, candidates receive subtitles in various languages. In the end, the learner will receive the course certificate, which is sharable on the LinkedIn profile.

4: Will I get any added benefit for subscribing to this Self-Driving Cars course?

Yes, you will receive a host of benefits, but the biggest is the e-Certificate of Specialization. The Certificate is sharable on your LinkedIn profile and will boost your credibility in the field. Additionally, you will have access to curated projects and quizzes.

5: What are the services offered by Coursera?

Coursera has a dedicated team working on various issues and queries. They provide support for privacy questions, business and government inquiries, industry partnership enquiries, university partnerships inquiries and press inquiries.