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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
Length 6 weeks
Effort 4 weeks of study, 5-6 hours per week
Starts Jul 3 (42 weeks ago)
Cost $49
From University of Toronto via Coursera
Instructors Jonathan Kelly, Steven Waslander
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Art & Design
Tags Computer Science Design And Product Software Development

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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.

This is the best course that can give me a in-depth understanding on Kalman Filter.

great excellent course 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.

This course is a hands-on approach to the development and implementation of the Kalman Filter for localization.

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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.

Also, for the final project the formulas have been given.

Also, the final project is very enlightening.

Final project is very nice.

Parts of the assignments and the final project were challenging and the course needs a lot of self-study.

The final project is difficult, you are expected to read some advanced papers on state estimation, but it is very rewarding once you figure out on your own.

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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.

The projects are useful enough This is a fast paced course on state estimation.

But I finished the course with the feeling that I have a lot to learn in the space of localization and state estimation.

An excellent course on state estimation and localization.

Great course that teaches you most of what you need to know about state estimation.

What is missing is the state estimation using particle filter, it would be great if there is a module dedicated for that.

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sensor fusion

I learned how to implement multiple sensor fusion into practice.

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

Very interesting course if you want to learn about the different filters used in self driving cars for sensor fusion Very useful!Great experience!Congratulation all the people involved in this course!

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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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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

State Auditor 2 $52k

Estimation Engineer $63k

Specialist, Cost Estimation $64k

Researcher, Cancer Estimation $67k

Estimation/Business Development - Energy $82k

State Officer, State Exchanges $84k

Applications Services Pricing and Estimation Leader $91k

Research Engineer (Robotics/Estimation) $114k

State Estimation and Calibration Scientist, PhD University Grad $124k

State Attorney $138k

Service Estimation Team Leader $140k

State Estimation and Calibration Scientist $169k

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Rating 4.4★ based on 39 ratings
Length 6 weeks
Effort 4 weeks of study, 5-6 hours per week
Starts Jul 3 (42 weeks ago)
Cost $49
From University of Toronto via Coursera
Instructors Jonathan Kelly, Steven Waslander
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Art & Design
Tags Computer Science Design And Product Software Development

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