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Learner Reviews & Feedback for State Estimation and Localization for Self-Driving Cars by University of Toronto

4.7
stars
831 ratings

About the 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 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)....

Top reviews

GN

Oct 30, 2019

best online course so far that explains kalman filter and estimation methods with examples not just focusing on theoretical ,Thanks to the Dr's and course staff who worked hard to produce this course.

YC

Mar 10, 2019

Could we use C++ to program the projects?And also, in most assignments, please make sure every requirements and additional information are CORRECT and CLEAR! Now, some of them are REALLY MISLEADING!

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126 - 133 of 133 Reviews for State Estimation and Localization for Self-Driving Cars

By Artod

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Sep 21, 2021

IMHO, assignments throughout all courses of the specialization are overcomplicated. I would appreciate more insights and intuitions about the subject rather than pain from calculation Jacobians and translating raw math formulas to Python code.

By Salma S L

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Mar 26, 2020

some equations weren't explained and remained ambiguous to me, needs more explanation on the mathematical side, other than that a great course and great effort

By Mustafa P

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Jan 26, 2021

More help should be provided by better lectures and more explanation on the projects.

By Wentao T

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May 17, 2020

too hard, and the data is not good

By Bourama T

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Jun 22, 2022

good course

By PRATIK W

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Aug 24, 2020

The coding part for each assignment should be explained in more detail

By Yousef D

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Aug 18, 2024

Poor explanation by the instructor

By LI Y

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Dec 12, 2022

Week 4 assignment grader is broken. Cannot get the cert because of this stupid error from Coursera.