State Estimation and Localization for Self-Driving Cars

Go to Course: https://www.coursera.org/learn/state-estimation-localization-self-driving-cars

Introduction

### Course Review: State Estimation and Localization for Self-Driving Cars on Coursera #### Overview The "State Estimation and Localization for Self-Driving Cars" course offered by the University of Toronto is an enlightening and essential component of their Self-Driving Cars Specialization. This second course builds on the fundamentals laid out in the first course, and is designed to equip learners with an in-depth understanding of how self-driving cars ascertain their position and movement in the world using various sensors. The course is structured to introduce you to the critical aspects of state estimation and localization, which are indispensable for the development of safe and reliable autonomous vehicles. By the end of the course, you will not only understand the essential methods for parameter and state estimation but also develop a functioning state estimator using cutting-edge technologies. #### Course Content and Structure The course encompasses five modules, each catering to specific aspects of state estimation and localization. 1. **Module 0: Welcome to Course 2** The introductory module sets the stage by motivating the challenges involved in estimating a vehicle's state and position. It emphasizes the need for precise localization for safe driving, making it clear why this subject is crucial for anyone interested in autonomous vehicle technology. 2. **Module 1: Least Squares** This foundational module reviews the method of least squares—a cornerstone for many statistical techniques, including those used in self-driving technology. It provides a comprehensive understanding of both unweighted and weighted observations, establishing the connection between least squares and maximum likelihood estimators. 3. **Module 2: State Estimation - Linear and Nonlinear Kalman Filters** Here, learners delve into one of the most important algorithms in modern technology: the Kalman filter. This module not only covers the derivation of the Kalman filter equations but also extends to the nonlinear realm with the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), crucial tools for the estimation processes in autonomous systems. 4. **Module 3: GNSS/INS Sensing for Pose Estimation** Understanding how to effectively use Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS) is vital for pose estimation. This module dives into sensor models and performance characteristics that enable learners to combine data sources for robust positioning. 5. **Module 4: LIDAR Sensing** LIDAR technology is a game-changer in the field of self-driving cars. In this module, learners explore LIDAR sensor models and how to process the data to create 3D point clouds. This optimistic approach to understanding how LIDAR can enhance state estimations makes it integral for future autonomous systems. 6. **Module 5: Putting It Together - An Autonomous Vehicle State Estimator** The final module integrates all previous learnings into the development of a full vehicle state estimator using the CARLA simulator. This practical application helps cement knowledge by building an estimator that processes multiple sensor inputs and evaluates system performance under various conditions. #### Recommendations The "State Estimation and Localization for Self-Driving Cars" course comes highly recommended for anyone pursuing a career in autonomous vehicle technology, robotics, or systems engineering. Here are a few reasons why you should consider enrolling: 1. **Expert-Led**: With instruction from the University of Toronto's esteemed faculty, you are sure to receive a high-quality education based on current industry standards. 2. **Comprehensive Curriculum**: The course is well-structured, guiding learners from theoretical knowledge to practical application, making it suitable for both newcomers and professionals looking to deepen their expertise. 3. **Hands-On Learning**: The practical component using the CARLA simulator ensures that you apply theoretical concepts in real-world scenarios, enhancing your understanding and skills. 4. **Career Relevance**: Given the rapid advancement of autonomous vehicle technology, understanding state estimation and localization will position you favorably in this burgeoning field. In conclusion, this course is a vital stepping stone for anyone serious about working on self-driving cars and automated systems. With a blend of theoretical foundation and practical application, you will emerge confident in your ability to contribute to this exciting area of technology. If you're aiming to enhance your knowledge and skills in autonomous vehicles, I wholeheartedly recommend you enroll in this course.

Syllabus

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. An accurate estimate of the vehicle state and its position on the road is required at all times to drive safely.

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.

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.

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.

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).

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.