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Sebastian Thrun,
Julia Chernushevich,
Karim Chamaa,
and
David Silver
Learn how Gaussian filters can be used to estimate noisy sensor readings, and how to estimate a robot’s position relative to a known map of the environment with Monte Carlo Localization (MCL).
Register for this course and see more details by visiting:
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What's inside
Syllabus
Introduction to the localization concept and the algorithms
Learn the Kalman Filter and Extended Kalman Filter Gaussian estimation algorithms.
Read more
Syllabus
Introduction to the localization concept and the algorithms
Learn the Kalman Filter and Extended Kalman Filter Gaussian estimation algorithms.
Read more
Learn how to apply an EKF ROS package to a robot to estimate its pose.
Learn the Monte Carlo Localization algorithm which uses particle filters to estimate a robot's pose.
Learn how to code the Monte Carlo Localization algorithm in C++.
Use the Adaptive Monte Carlo Localization algorithm in ROS to localize your robot!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills essential for Robotics Engineers and People in other technical fields who work with sensors and localization systems
Engages learners in topics that are highly relevant to the field of Robotics
Taught by seasoned professionals who are recognized experts in their field
Suitable for learners with a technical background who are interested in Robotics
Provides a solid foundation for those who are interested in developing localization systems
Offers hands-on exercises that reinforce concepts learned in the course
Save this course
Save Localization to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Localization with these
activities:
Create a Course Notes and Resources Repository
Show steps
Organize and consolidate your course materials for easy reference and revision.
Show steps
-
Compile your notes, slides, assignments, and any additional resources.
-
Create a digital or physical repository for easy access.
Read 'Probabilistic Robotics' by Thrun, Burgard, and Fox
Show steps
Gain a deeper understanding of the theoretical foundations and practical applications of robotics and localization.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
-
Read the chapters relevant to Gaussian filters, Kalman Filter, and Monte Carlo Localization.
-
Work through the exercises and examples provided in the book.
Form a Study Group with Classmates
Show steps
Enhance your learning experience by collaborating with peers through a study group.
Browse courses on
Robot Localization
Show steps
-
Find classmates who are interested in forming a study group.
-
Establish regular meeting times and discuss course materials.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a Visual Guide to Gaussian Filters
Show steps
Reinforce your understanding of Gaussian filters by creating a visual guide that explains their principles, applications, and benefits.
Show steps
-
Research Gaussian filters and their properties.
-
Design a visual representation of how Gaussian filters work.
-
Create a guide that includes examples and applications of Gaussian filters.
Practice Kalman Filter and Monte Carlo Localization Algorithms
Show steps
Improve your understanding and proficiency in applying Kalman Filter and Monte Carlo Localization algorithms for robot localization.
Browse courses on
Kalman Filter
Show steps
-
Review the course materials on Kalman Filter and Monte Carlo Localization.
-
Implement the Kalman Filter algorithm in Python or C++.
-
Implement the Monte Carlo Localization algorithm in Python or C++.
-
Simulate different scenarios and test the performance of your algorithms.
Attend a Workshop on Robot Localization with ROS
Show steps
Complement your online learning by attending a workshop that provides hands-on experience with robot localization techniques.
Browse courses on
Robot Localization
Show steps
-
Find and register for a workshop on robot localization.
-
Attend the workshop and actively participate in the exercises.
Follow Online Tutorials on Extended Kalman Filters
Show steps
Expand your knowledge by exploring tutorials that cover the more advanced concepts of Extended Kalman Filters.
Browse courses on
Extended Kalman Filter
Show steps
-
Search for online tutorials on Extended Kalman Filters.
-
Follow the tutorials and implement the techniques in your own code.
Build a Robot Localization System Using MCL
Show steps
Apply your knowledge of Monte Carlo Localization to a practical project by building a robot localization system.
Browse courses on
Robot Localization
Show steps
-
Design and implement a robot localization system using MCL.
-
Integrate sensors such as odometry, IMU, and lidar.
-
Test and evaluate the performance of your system in different environments.
Create a Course Notes and Resources Repository
Show steps
Organize and consolidate your course materials for easy reference and revision.
Show steps
Show steps
Show steps
- Compile your notes, slides, assignments, and any additional resources.
- Create a digital or physical repository for easy access.
Read 'Probabilistic Robotics' by Thrun, Burgard, and Fox
Show steps
Gain a deeper understanding of the theoretical foundations and practical applications of robotics and localization.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to Gaussian filters, Kalman Filter, and Monte Carlo Localization.
- Work through the exercises and examples provided in the book.
Form a Study Group with Classmates
Show steps
Enhance your learning experience by collaborating with peers through a study group.
Browse courses on
Robot Localization
Show steps
Show steps
Show steps
- Find classmates who are interested in forming a study group.
- Establish regular meeting times and discuss course materials.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a Visual Guide to Gaussian Filters
Show steps
Reinforce your understanding of Gaussian filters by creating a visual guide that explains their principles, applications, and benefits.
Show steps
Show steps
Show steps
- Research Gaussian filters and their properties.
- Design a visual representation of how Gaussian filters work.
- Create a guide that includes examples and applications of Gaussian filters.
Practice Kalman Filter and Monte Carlo Localization Algorithms
Show steps
Improve your understanding and proficiency in applying Kalman Filter and Monte Carlo Localization algorithms for robot localization.
Browse courses on
Kalman Filter
Show steps
Show steps
Show steps
- Review the course materials on Kalman Filter and Monte Carlo Localization.
- Implement the Kalman Filter algorithm in Python or C++.
- Implement the Monte Carlo Localization algorithm in Python or C++.
- Simulate different scenarios and test the performance of your algorithms.
Attend a Workshop on Robot Localization with ROS
Show steps
Complement your online learning by attending a workshop that provides hands-on experience with robot localization techniques.
Browse courses on
Robot Localization
Show steps
Show steps
Show steps
- Find and register for a workshop on robot localization.
- Attend the workshop and actively participate in the exercises.
Follow Online Tutorials on Extended Kalman Filters
Show steps
Expand your knowledge by exploring tutorials that cover the more advanced concepts of Extended Kalman Filters.
Browse courses on
Extended Kalman Filter
Show steps
Show steps
Show steps
- Search for online tutorials on Extended Kalman Filters.
- Follow the tutorials and implement the techniques in your own code.
Build a Robot Localization System Using MCL
Show steps
Apply your knowledge of Monte Carlo Localization to a practical project by building a robot localization system.
Browse courses on
Robot Localization
Show steps
Show steps
Show steps
- Design and implement a robot localization system using MCL.
- Integrate sensors such as odometry, IMU, and lidar.
- Test and evaluate the performance of your system in different environments.
Career center
Learners who complete Localization will develop knowledge and skills
that may be useful to these careers:
Robotics Engineer
Robotics Engineer
Robotics Engineers are responsible for designing, building, and maintaining robots, which are used in a wide variety of industries, including manufacturing, healthcare, and space exploration. This course provides a foundation in the algorithms and techniques used to localize robots, which is a critical skill for Robotics Engineers. By taking this course, you will gain the skills and knowledge necessary to develop robots that can navigate their environment safely and efficiently.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers are responsible for designing, building, and testing self-driving cars and other autonomous vehicles. This course provides a foundation in the algorithms and techniques used to localize autonomous vehicles, which is a critical skill for Autonomous Vehicle Engineers. By taking this course, you will gain the skills and knowledge necessary to develop autonomous vehicles that can navigate their environment safely and efficiently.
Navigation Engineer
Navigation Engineer
Navigation Engineers are responsible for designing and maintaining navigation systems for robots. This course provides a foundation in the algorithms and techniques used to estimate a robot’s position relative to a known map of the environment. These skills are essential for Navigation Engineers who work with robots and other autonomous systems.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers are responsible for designing and maintaining control systems for robots. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Control Systems Engineers who work with robots and other autonomous systems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers are responsible for developing and maintaining computer vision systems for robots. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Computer Vision Engineers who work with robots and other autonomous systems.
Sensor Engineer
Sensor Engineer
Sensor Engineers are responsible for designing, building, and maintaining sensors, which are used to collect data from the environment. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings, which is a critical skill for Sensor Engineers. By taking this course, you will gain the skills and knowledge necessary to develop sensors that can provide accurate and reliable data.
Robotics Software Engineer
Robotics Software Engineer
Robotics Software Engineers are responsible for developing and maintaining software for robots. This course provides a foundation in the algorithms and techniques used to localize robots, which is a critical skill for Robotics Software Engineers. By taking this course, you will gain the skills and knowledge necessary to develop software that allows robots to navigate their environment safely and efficiently.
Perception Engineer
Perception Engineer
Perception Engineers are responsible for developing and maintaining perception systems for robots. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Perception Engineers who work with robots and other autonomous systems.
Planning Engineer
Planning Engineer
Planning Engineers are responsible for developing and maintaining planning systems for robots. This course provides a foundation in the algorithms and techniques used to estimate a robot’s position relative to a known map of the environment. These skills are essential for Planning Engineers who work with robots and other autonomous systems.
Research Scientist
Research Scientist
Research Scientists are responsible for conducting research in a variety of fields, including robotics, computer vision, and machine learning. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Research Scientists who work with robots and other autonomous systems.
Systems Architect
Systems Architect
Systems Architects are responsible for designing and maintaining systems of systems. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Systems Architects who work with robots and other autonomous systems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Machine Learning Engineers who work with robots and other autonomous systems.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills are essential for Data Scientists who work with data from robots and other autonomous systems.
Technical Program Manager
Technical Program Manager
Technical Program Managers are responsible for managing technical programs. This course provides a foundation in the algorithms and techniques used to estimate noisy sensor readings and to estimate a robot’s position relative to a known map of the environment. These skills may be useful for Technical Program Managers who work with robots and other autonomous systems.
Product Manager
Product Manager
Product Managers are responsible for managing products. This course may be useful for Product Managers who work with robots and other autonomous systems.
For more career information including salaries, visit:
OpenCourser.com/course/lrq9kb/localizatio
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Localization.
This comprehensive textbook provides a thorough foundation in probabilistic robotics, covering topics such as Gaussian filters, Kalman filters, and Monte Carlo localization. It valuable reference for gaining a deeper understanding of the concepts covered in the course.
This textbook offers a comprehensive overview of autonomous mobile robots, including topics such as localization, navigation, and path planning. It provides a good foundation for understanding the broader context of the course's topics.
This textbook focuses on the real-time aspects of robotics, including topics such as Kalman filtering and state estimation. It provides valuable insights into the practical implementation of localization algorithms.
Provides a comprehensive introduction to Monte Carlo methods and their applications in Bayesian computation. It good resource for gaining a deeper understanding of the theoretical foundations of particle filters.
Provides a comprehensive introduction to ROS, which widely used robotics middleware. It useful resource for understanding the software tools and techniques used in robot localization.
Provides a comprehensive introduction to Gaussian processes, which are a powerful machine learning technique that can be used for regression, classification, and other tasks. It good resource for gaining a deeper understanding of the theoretical foundations of Gaussian filters.
Save
This textbook provides a comprehensive overview of mobile robotics, including topics such as localization, navigation, and path planning. It good resource for gaining a broader understanding of the field of robotics.
Provides a comprehensive introduction to Bayesian filtering and smoothing, which are powerful techniques for estimating the state of a dynamic system from noisy observations. It good resource for gaining a deeper understanding of the theoretical foundations of Kalman filters and particle filters.
This textbook provides a comprehensive introduction to robotics. It covers a wide range of topics, including robot kinematics, dynamics, and control. It good resource for gaining a broader understanding of the field of robotics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lrq9kb/localizatio
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Time
30240 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Julia Chernushevich
Karim Chamaa
David Silver
Language
English
Good to know
Develops skills essential for Robotics Engineers and People in other technical fields who work with sensors and localization systems
Engages learners in topics that are highly relevant to the field of Robotics
Taught by seasoned professionals who are recognized experts in their field
Suitable for learners with a technical background who are interested in Robotics
Provides a solid foundation for those who are interested in developing localization systems
Offers hands-on exercises that reinforce concepts learned in the course
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