University of Toronto
State Estimation and Localization for Self-Driving Cars
University of Toronto

State Estimation and Localization for Self-Driving Cars

This course is part of Self-Driving Cars Specialization

Taught in English

Some content may not be translated

Jonathan Kelly
Steven Waslander

Instructors: Jonathan Kelly

49,882 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(811 reviews)

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95%

Advanced level

Recommended experience

26 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(811 reviews)

|

95%

Advanced level

Recommended experience

26 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Self-Driving Cars Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course

This module introduces you to the main concepts discussed in the course and presents the layout of the course. The module describes and motivates the problems of state estimation and localization for self-driving cars. An accurate estimate of the vehicle state and its position on the road is required at all times to drive safely.

What's included

9 videos3 readings1 discussion prompt

The method of least squares, developed by Carl Friedrich Gauss in 1795, is a well known technique for estimating parameter values from data. This module provides a review of least squares, for the cases of unweighted and weighted observations. There is a deep connection between least squares and maximum likelihood estimators (when the observations are considered to be Gaussian random variables) and this connection is established and explained. Finally, the module develops a technique to transform the traditional 'batch' least squares estimator to a recursive form, suitable for online, real-time estimation applications.

What's included

4 videos3 readings3 quizzes2 ungraded labs

Any engineer working on autonomous vehicles must understand the Kalman filter, first described in a paper by Rudolf Kalman in 1960. The filter has been recognized as one of the top 10 algorithms of the 20th century, is implemented in software that runs on your smartphone and on modern jet aircraft, and was crucial to enabling the Apollo spacecraft to reach the moon. This module derives the Kalman filter equations from a least squares perspective, for linear systems. The module also examines why the Kalman filter is the best linear unbiased estimator (that is, it is optimal in the linear case). The Kalman filter, as originally published, is a linear algorithm; however, all systems in practice are nonlinear to some degree. Shortly after the Kalman filter was developed, it was extended to nonlinear systems, resulting in an algorithm now called the ‘extended’ Kalman filter, or EKF. The EKF is the ‘bread and butter’ of state estimators, and should be in every engineer’s toolbox. This module explains how the EKF operates (i.e., through linearization) and discusses its relationship to the original Kalman filter. The module also provides an overview of the unscented Kalman filter, or UKF, a more recently developed and very popular member of the Kalman filter family.

What's included

6 videos5 readings1 programming assignment1 ungraded lab

To navigate reliably, autonomous vehicles require an estimate of their pose (position and orientation) in the world (and on the road) at all times. Much like for modern aircraft, this information can be derived from a combination of GPS measurements and inertial navigation system (INS) data. This module introduces sensor models for inertial measurement units and GPS (and, more broadly, GNSS) receivers; performance and noise characteristics are reviewed. The module describes ways in which the two sensor systems can be used in combination to provide accurate and robust vehicle pose estimates.

What's included

4 videos3 readings1 quiz

LIDAR (light detection and ranging) sensing is an enabling technology for self-driving vehicles. LIDAR sensors can ‘see’ farther than cameras and are able to provide accurate range information. This module develops a basic LIDAR sensor model and explores how LIDAR data can be used to produce point clouds (collections of 3D points in a specific reference frame). Learners will examine ways in which two LIDAR point clouds can be registered, or aligned, in order to determine how the pose of the vehicle has changed with time (i.e., the transformation between two local reference frames).

What's included

4 videos3 readings1 quiz

This module combines materials from Modules 1-4 together, with the goal of developing a full vehicle state estimator. Learners will build, using data from the CARLA simulator, an error-state extended Kalman filter-based estimator that incorporates GPS, IMU, and LIDAR measurements to determine the vehicle position and orientation on the road at a high update rate. There will be an opportunity to observe what happens to the quality of the state estimate when one or more of the sensors either 'drop out' or are disabled.

What's included

8 videos2 readings1 programming assignment1 discussion prompt

Instructors

Instructor ratings
4.7 (153 ratings)
Jonathan Kelly
University of Toronto
4 Courses162,047 learners
Steven Waslander
University of Toronto
4 Courses162,047 learners

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