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    Quick Facts

    Medium Of InstructionsMode Of LearningMode Of Delivery
    EnglishSelf StudyVideo 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

    Program 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 AvailabilityCertificate Providing Authority
    yesCoursera
    • 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

    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.

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