# State Estimation and Localization for Self-Driving Cars

Self-Driving Cars,

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).

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Rating 4.4★ based on 39 ratings 6 weeks 4 weeks of study, 5-6 hours per week Nov 7 (20 weeks ago) \$49 University of Toronto via Coursera Jonathan Kelly, Steven Waslander On all desktop and mobile devices English Programming Art & Design Computer Science Design And Product Software Development

## What people are saying

kalman filter

Got to learn about many concepts like least squares, Kalman filter, GNSS/INS sensing, LIDAR Sensing.

Lectures cover basics of Kalman filter very thoroughly.

final project

There is/was an expectation of doing the final project in CARLA online but it was offline and also the ICP was pre-implemented.

ES Kalman Filter is the focus of the final project.

state estimation

But overall for starters it is a very good course for state estimation to support and I strongly suggest to complete it if you aspire to be a self - driving car engineer.

There are several errors in the presentations and in the videos, the tutors did not correct them and thus the assignments were very confusing due to stupid math mistakes made by the organizers, it is clear that they are not taking it 100% serious, nonetheless I have seen few courses were they explain State estimation for SDV so good as this one.

sensor fusion

I learned how to implement multiple sensor fusion into practice.

a very good course about sensor fusion ans localization Excellent course!

excellent course

I enjoyed it excellent course with a lot of valuable and up to date information that is used in real modern self driving cars, it was challenging and very hard for me to go through but i assure you that it's worthy of the hard work required to pass it Challenging course, specially the assignments.

little bit

Personally I found the coding assignments really demanding and as a side note I would have appreciated a little bit more presence of the teaching stuff to clarify.

All in all I am a little bit mixed about the course as for example particle filters are just mentioned in one video but not explained as all the various types of Kalman filters.

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