State Estimation and Localization for Self-Driving Cars

National Taiwan University via Coursera

Overview

Welcome to the dynamic realm of State Estimation and Localization for Self-Driving Cars, a pivotal course offered by the University of Toronto, designed as part of their comprehensive Self-Driving Cars Specialization. Embark on a journey that promises to equip you with profound knowledge and skills in employing various sensors for the accurate state estimation and localization vital in the operation of self-driving vehicles.

This advanced course serves as an essential step for learners keen on delving deeper into the autonomous driving domain, recommending completion of the specialization's initial course for a seamless learning experience. Throughout this curriculum, you will be introduced to an array of sensor technologies and methodologies crucial for parameter and state estimation, which are foundational for autonomous driving. Key highlights include:

A deep dive into the method of least-squares and other paramount techniques for parameter and state estimation.
Constructing models for typical vehicle localization sensors, such as GPS and IMUs, to understand their functionality and integration.
Mastering the application of advanced algorithms like extended and unscented Kalman Filters for robust vehicle state estimation.
Exploring LIDAR scan matching and the Iterative Closest Point algorithm to enhance localization accuracy.
Learning to fuse data from multiple sensor streams into a cohesive state estimate, a critical skill for developing autonomous vehicles.

For the capstone project, you will take on the challenge of implementing the Error-State Extended Kalman Filter (ES-EKF) using data from the CARLA simulator, solidifying your skills in vehicle localization. This course is tailored for individuals with a solid foundation in mechanical engineering, computer and electrical engineering, or robotics, and a proficiency in Python 3.0. Additionally, a good understanding of Linear Algebra, Statistics, Calculus, and Physics is required to fully benefit from this course. Offered by the prestigious University of Toronto and available on Coursera, this course falls under the categories of Autonomous Vehicles and Kalman Filter Courses, setting a benchmark for specialization in the field.