Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course.
Этот курс входит в специализацию ''Специализация Беспилотные автомобили'
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Об этом курсе
This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.
Чему вы научитесь
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
This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.
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Программа курса: что вы изучите
Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars
Module 1: Least Squares
Module 2: State Estimation - Linear and Nonlinear Kalman Filters
Module 3: GNSS/INS Sensing for Pose Estimation
Module 4: LIDAR Sensing
Рецензии
- 5 stars78,09 %
- 4 stars15,97 %
- 3 stars3,99 %
- 2 stars0,38 %
- 1 star1,54 %
Лучшие отзывы о курсе STATE ESTIMATION AND LOCALIZATION FOR SELF-DRIVING CARS
Finishing this course was quite challenging, but I did it. Thanks a lot to the professors for the clear explanations.
A well-taught course by Prof. Jonathan Kelly.I accumulated huge amount of knowledge after undergoing his teachings.The supplementary readings proved to be of great help to ace the final project.
great course but there's really a big need to provide assistance in assignments like hints, equations etc
one of best experiences. But the course requires a steep learning curve. The discussion forums are really helpful
Специализация Беспилотные автомобили: общие сведения

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