The virtual course "Estimation and location of states for autonomous cars - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 27 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Welcome to State Estimation and Location for Autonomous Cars, the second course in the Autonomous Cars specialization at the University of Toronto.
We recommend that you take the first course in the Specialization before taking this course.
This course will introduce you to the different sensors and how we can use them for state estimation and localization in an autonomous car.
At 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 least squares method - Develop a model for typical vehicle location sensors, including GPS and IMU - Apply Unscented and extended Kalman filters to a vehicle state estimation problem - Understand lidar scan matching and the iterative nearest point algorithm - Apply these tools to merge multiple sensor streams into a single state estimate for an autonomous car For the final project of this course, you will implement the Error State Extended Kalman Filter (ES-EKF) to locate a vehicle using data from the CARLA simulator.
This is an advanced course, intended for students with backgrounds in mechanical engineering, computer science, and electrical or robotic engineering.
To be successful in this course, you must have programming experience in Python 3.
0, be familiar with linear algebra (matrices, vectors, matrix multiplication, range, eigenvalues and vectors and inverses), statistics (Gaussian probability distributions),
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