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U of T - State Estimation and Localization for Self-Driving Cars 

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State Estimation and Localization for Self-Driving Cars
 at 
Coursera 
Overview

Duration

23 hours

Mode of learning

Online

Difficulty level

Advanced

Credential

Certificate

State Estimation and Localization for Self-Driving Cars
Table of contents
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State Estimation and Localization for Self-Driving Cars
 at 
Coursera 
Highlights

  • 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
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State Estimation and Localization for Self-Driving Cars
 at 
Coursera 
Course details

Skills you will learn
PythonStatistics
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).

State Estimation and Localization for Self-Driving Cars
 at 
Coursera 
Curriculum

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/INS Sensing for Pose Estimation

Module 4: LIDAR Sensing

Module 5: Putting It together - An Autonomous Vehicle State Estimator

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State Estimation and Localization for Self-Driving Cars
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State Estimation and Localization for Self-Driving Cars
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