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State Estimation and Localization for Self-Driving Cars

State Estimation and Localization for Self-Driving Cars

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29 hours
English
University of Toronto
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is from one of the top universities of the world - University of Toronto.

Price

This course costs very less.

Difficulty

The students of this course have found this course difficult.

Content

The students of this course have liked the content of this course.

Assignments

The students of this course have liked the assignments of this course.

Teaching

The students of this course have liked how the instructor has taught this course.

Satisfaction

The students of this course are overall satisfied with this course.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

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 course 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. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws).

Syllabus

Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars
Module 1: Least Squares
Module 2: State Estimation - Linear and Nonlinear Kalman Filters
Module 3: GNSS\u002FINS Sensing for Pose Estimation
Module 4: LIDAR Sensing
Module 5: Putting It together - An Autonomous Vehicle State Estimator

What others say about this course

Reviews from Coursera

There is no support for this classThe forums are almost useless and no teacher or staff ever answers anything on themThe lectures are pure fluff and hand-waving, no meat and no detailsThe projects are   Read More ...

one of best experiences. But the course requires a steep learning curve. The discussion forums are really helpful

Very useful!Great experience!Congratulation all the people involved in this course!

Review :Mentor Help: 0/5Course Content: 4/5Course Explanation: 4/5Course Challenging: 4/5Exercises : 3/5Things which can be improved: There should be a programming exercise for each module especially f  Read More ...

Very dry lectures!Quiz automated grader buggy and not working at times. Example: not well defined python environment for the quiz in module 4. A grader expects a certain format that you have to guess.   Read More ...

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