University of Toronto - State Estimation and Localization for Self-Driving Cars
4.0 /5- Offered byCoursera
State Estimation and Localization for Self-Driving Cars at Coursera Overview
State Estimation and Localization for Self-Driving Cars
at Coursera
Duration | 23 hours |
Mode of learning | Online |
Difficulty level | Advanced |
Credential | Certificate |
State Estimation and Localization for Self-Driving Cars at Coursera Highlights
State Estimation and Localization for Self-Driving Cars
at Coursera
- 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
State Estimation and Localization for Self-Driving Cars
at Coursera
Skills you will learn
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
State Estimation and Localization for Self-Driving Cars
at Coursera
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 at Coursera Students Ratings & Reviews
State Estimation and Localization for Self-Driving Cars
at Coursera
4/5
1 Rating- 3-41
A
Aniket Gujarathi
State Estimation and Localization for Self-Driving Cars
Offered by Coursera
4
Other: 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.
Reviewed on 24 Apr 2020Read More
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State Estimation and Localization for Self-Driving Cars
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