State Estimation and Localization for Self-Driving Cars

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Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization.

This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to:
- Understand commonly used hardware used for self-driving cars
- Identify the main components of the self-driving software stack
- Program vehicle modelling and control
- Analyze the safety frameworks and current industry practices for vehicle development

For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance.

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).

You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).

WEEK 1
2 hours to complete
Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars
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.
9 videos (Total 33 min) 3 readings

7 hours to complete
Module 1: Least Squares
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.
4 videos (Total 33 min) 3 readings 3 quizzes

WEEK 2
7 hours to complete
Module 2: State Estimation - Linear and Nonlinear Kalman Filters
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 a more recently developed and very popular member of the Kalman filter family.
6 videos (Total 53 min) 5 readings 1 quiz

WEEK 3
2 hours to complete
Module 3: GNSS/INS Sensing for Pose Estimation
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.
4 videos (Total 34 min) 3 readings 1 quiz

WEEK 4
2 hours to complete
Module 4: LIDAR Sensing
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).
4 videos (Total 48 min) 3 readings 1 quiz


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Cung cấp bởi: Coursera /  University of Toronto

Thời lượng: 27 giờ
Ngôn ngữ giảng dạy: Tiếng Anh
Chi phí: Miễn phí / 0
Đối tượng: Advanced

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