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State Estimation and Localization for Self-Driving Cars

State Estimation and Localization for Self-Driving Cars

1 Reviews
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₹ 3,493

Course Highlights

  • 23 hours of E-learning content, tests etc

  • Requires effort of 5-6 hours per week

  • Offered by University of Toronto

  • Learn from the expert faculty of University of Toronto

  • Earn a certificate from University of Toronto

Mode of learning: Online self study

Course Overview

What are the course deliverables ?
  • 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

  • Apply LIDAR scan matching and the Iterative Closest Point algorithm

More about this course
  • 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 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).

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    Reviews

    Average Rating

    4.7

    Based on 1 Ratings

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    Aniket Gujarathi

    This course is provided on Coursera by University of Toronto. It is a good course which gives an introduction to the problem of localization in self-driving cars and how to solve that using sensor fusion and probabilistic filters. The course also provides assignments, both McQ and programming, which can help in getting knowledge about the recent estimators used in self driving cars and how to implement them. This course can act as an introductory course for localization as it will introduce the use of sensor fusion, calibration, various filters like kalman filters, ekf, ukf etc. However, to get deeper mathematical knowledge about probabilistic robotics, reading other supplementary material is necessary. Overall it is a good course.
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