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Sebastian Thrun,
Andy Brown,
Jake Lussier,
Raffaello D'Andrea,
Angela Schoellig,
Nicholas Roy,
and
Sergei Lupashin
Register for this course and see more details by visiting:
OpenCourser.com/course/zfmhab/estimatio
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What's inside
Syllabus
Review basic probability and learn three approaches to state estimation for a stationary vehicle.
In this lesson you'll learn about the sensors a drone uses to localize itself in the world. You'll implement sensor models, analyze sources of error, and perform calibration of various sensors.
Read more
Syllabus
Review basic probability and learn three approaches to state estimation for a stationary vehicle.
In this lesson you'll learn about the sensors a drone uses to localize itself in the world. You'll implement sensor models, analyze sources of error, and perform calibration of various sensors.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops core skills for engineers working with autonomous flight systems
Taught by leading experts in robotics and autonomous systems
Provides opportunities to apply theoretical concepts in practical projects
Includes a mix of video lectures, hands-on exercises, and discussions
Requires a strong foundation in probability and linear algebra
Assumes familiarity with basic principles of autonomous flight
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Reviews summary
Practical estimation for autonomous systems
According to students, this course provides a strong foundation in sensor fusion and state estimation, particularly focusing on the Extended Kalman Filter (EKF) for autonomous systems. Learners frequently highlight the challenging yet highly practical quadrotor project as a key strength, offering invaluable hands-on experience with IMU and GPS data. While praised for its clarity in explaining complex topics, some learners found the pacing of certain advanced concepts like the Unscented Kalman Filter quite rapid. Prospective students should be aware that a solid background in linear algebra and probability is essential to navigate the rigorous content successfully, making it best suited for career-focused individuals in robotics or control systems.
The course materials and code examples have been updated over time.
"Some of the older code examples in the labs were tricky to get running, but it seems they've been updated since I took it."
"I noticed improvements in the clarity of some diagrams compared to older versions my colleagues used."
"The resources provided felt current, showing continuous attention from the instructors to maintain relevance."
Complex topics like EKF are explained with exceptional clarity.
"The explanations of the EKF were incredibly clear, and the project was challenging but truly solidified my understanding."
"Outstanding course! The instructor breaks down complex concepts like the Kalman filter beautifully."
"I appreciate how the derivations were presented, making abstract ideas much more concrete and understandable."
The capstone project provides invaluable hands-on application.
"The project was challenging but truly solidified my understanding and applied all the theory in a very tangible way."
"The quadrotor project was a highlight and made the entire course worthwhile for practical learning."
"The hands-on experience with real sensor data was exactly what I needed to grasp these concepts deeply."
Some advanced topics like UKF felt rushed and could use more depth.
"Some parts felt a bit rushed, particularly the UKF section; I had to consult external resources for a deeper dive."
"The jump into 3D EKF felt very steep and could benefit from more guided practice problems."
"While comprehensive, I wished for more detailed explanations on certain complex derivations to fully grasp them."
A solid background in linear algebra and probability is crucial.
"The prerequisites are no joke – you absolutely need strong linear algebra and probability to keep up."
"I struggled a lot with this course as it assumed a higher level of prior knowledge than I possessed."
"I found myself rewatching the dense lectures multiple times; it definitely requires a solid math background."
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 Estimation with these
activities:
Review Control Systems
Show steps
Reviewing Control Systems will help solidify understanding of the fundamentals and prepare you for the more advanced topics covered in this course.
Browse courses on
Control Systems
Show steps
-
Review basic concepts like stability, performance, and feedback loops.
-
Solve practice problems related to control system design.
-
Use simulation tools to analyze and design control systems.
Review Probabilistic Robotics
Show steps
This book provides a comprehensive overview of probabilistic robotics, which is essential for understanding the concepts covered in this course.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
-
Read the book's chapters relevant to the course topics.
-
Solve exercises and problems from the book.
State Estimation Study Group
Show steps
Participating in a study group will allow you to engage with other students, discuss concepts, and clarify your understanding.
Show steps
-
Form a study group with other students taking the course.
-
Meet regularly to discuss course concepts, work on problems, and share insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Sensor Fusion Practice
Show steps
Regular practice with sensor fusion problems will help improve your understanding and problem-solving skills.
Show steps
-
Solve practice problems involving sensor fusion using Kalman filters.
-
Implement sensor fusion algorithms in a simulated environment.
Create a Sensor Fusion Tutorial
Show steps
Creating a tutorial will force you to organize your knowledge and deepen your understanding of the topic.
Show steps
-
Choose a specific aspect of sensor fusion to focus on.
-
Develop a clear and concise explanation of the concept.
-
Create a tutorial in written, video, or interactive format.
-
Share your tutorial with other students or the online community.
EKF Estimator Implementation
Show steps
Following guided tutorials on EKF estimator implementation will provide practical experience and deepen your understanding.
Show steps
-
Find online tutorials or courses on EKF estimator implementation.
-
Follow the instructions and implement an EKF estimator in a simulation environment.
-
Use the implemented estimator to solve a real-world problem.
Drone State Estimation Project
Show steps
Working on a project involving drone state estimation will provide hands-on experience and reinforce the concepts learned in the course.
Browse courses on
State Estimation
Show steps
-
Design and implement an EKF estimator for a simulated drone.
-
Use the estimator to track the drone's state in a realistic simulation environment.
-
Present your project findings.
Contribute to an Open-Source Sensor Fusion Project
Show steps
Contributing to an open-source project will provide hands-on experience and expose you to real-world applications of sensor fusion.
Show steps
-
Find an open-source project related to sensor fusion that interests you.
-
Read the project's documentation and contribute code, documentation, or bug fixes.
-
Attend online discussions or meetups related to the project.
Review Control Systems
Show steps
Reviewing Control Systems will help solidify understanding of the fundamentals and prepare you for the more advanced topics covered in this course.
Browse courses on
Control Systems
Show steps
Show steps
Show steps
- Review basic concepts like stability, performance, and feedback loops.
- Solve practice problems related to control system design.
- Use simulation tools to analyze and design control systems.
Review Probabilistic Robotics
Show steps
This book provides a comprehensive overview of probabilistic robotics, which is essential for understanding the concepts covered in this course.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters relevant to the course topics.
- Solve exercises and problems from the book.
State Estimation Study Group
Show steps
Participating in a study group will allow you to engage with other students, discuss concepts, and clarify your understanding.
Show steps
Show steps
Show steps
- Form a study group with other students taking the course.
- Meet regularly to discuss course concepts, work on problems, and share insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Sensor Fusion Practice
Show steps
Regular practice with sensor fusion problems will help improve your understanding and problem-solving skills.
Show steps
Show steps
Show steps
- Solve practice problems involving sensor fusion using Kalman filters.
- Implement sensor fusion algorithms in a simulated environment.
Create a Sensor Fusion Tutorial
Show steps
Creating a tutorial will force you to organize your knowledge and deepen your understanding of the topic.
Show steps
Show steps
Show steps
- Choose a specific aspect of sensor fusion to focus on.
- Develop a clear and concise explanation of the concept.
- Create a tutorial in written, video, or interactive format.
- Share your tutorial with other students or the online community.
EKF Estimator Implementation
Show steps
Following guided tutorials on EKF estimator implementation will provide practical experience and deepen your understanding.
Show steps
Show steps
Show steps
- Find online tutorials or courses on EKF estimator implementation.
- Follow the instructions and implement an EKF estimator in a simulation environment.
- Use the implemented estimator to solve a real-world problem.
Drone State Estimation Project
Show steps
Working on a project involving drone state estimation will provide hands-on experience and reinforce the concepts learned in the course.
Browse courses on
State Estimation
Show steps
Show steps
Show steps
- Design and implement an EKF estimator for a simulated drone.
- Use the estimator to track the drone's state in a realistic simulation environment.
- Present your project findings.
Contribute to an Open-Source Sensor Fusion Project
Show steps
Contributing to an open-source project will provide hands-on experience and expose you to real-world applications of sensor fusion.
Show steps
Show steps
Show steps
- Find an open-source project related to sensor fusion that interests you.
- Read the project's documentation and contribute code, documentation, or bug fixes.
- Attend online discussions or meetups related to the project.
Career center
Learners who complete Estimation will develop knowledge and skills
that may be useful to these careers:
Controls Engineer
Controls Engineer
Controls Engineers design and implement systems to control autonomous machines, like drones. Understanding how to develop an Extended Kalman Filter, which is what this course teaches, is required knowledge for anyone in this field. By completing this course, you will gain the skills necessary to contribute to and enhance drone and autonomous vehicle systems.
Robotics Engineer
Robotics Engineer
Robotics Engineers apply their understanding of engineering fundamentals and computer science to design, build, and maintain robots. This course will teach you the concepts necessary for working with the IMU and GPS data that drones use to localize themselves. This is a key skill for working in the field of robotics.
Autonomy Engineer
Autonomy Engineer
Autonomy Engineers develop and implement software that enables vehicles to operate independently of human input. They design sensors, algorithms, and control systems to allow vehicles to perceive, understand, and respond to their environment. The skills you will learn in this course will be essential for a career in this field.
Software Engineer
Software Engineer
Software Engineers who specialize in autonomous systems develop the software algorithms and code that control self-driving cars, drones, and other autonomous vehicles. In this course, you'll develop an Extended Kalman Filter, which is essential for the success of any autonomous vehicle. It is also key for building a foundation in this field.
Aerospace Engineer
Aerospace Engineer
Aerospace Engineers design, develop, and test aircraft, spacecraft, and missiles, among other vehicles. This course can be useful for learning the fundamentals of navigation, which is an important part of designing and developing these systems.
Avionics Engineer
Avionics Engineer
Avionics Engineers design, develop, and maintain the electronic systems on aircraft, such as the navigation, communication, and control systems. This course can be useful for learning about some of the fundamentals of navigation, which is a core part of avionics.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to analyze data and extract meaningful insights. This course may help build a foundation in statistics and probability, which is essential for success in this field.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical systems, such as those used in drones and autonomous vehicles. This course will help you understand sensor fusion, filtering, and other techniques used in these systems.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts use their knowledge of geography and spatial data to solve problems, such as finding the best location for a new store or analyzing the impact of a natural disaster. This course can be useful for learning the fundamentals of GPS, which is a key technology used in this field.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design, develop, and maintain mechanical systems, such as those used in drones and autonomous vehicles. This course can be helpful for understanding the physical principles that govern the motion of these systems.
Product Manager
Product Manager
Product Managers are responsible for the development and marketing of new products or features. This course may help build a foundation in the underlying technology of autonomous systems, which can be helpful for understanding the needs of customers and stakeholders.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for ensuring that products or services meet the required standards. This course can be useful for learning some of the techniques used to test and validate autonomous systems.
Systems Engineer
Systems Engineer
Systems Engineers are responsible for the design, development, and maintenance of complex systems, such as those used in drones and autonomous vehicles. This course may help build a foundation in some of the underlying technologies used in these systems.
Technical Writer
Technical Writer
Technical Writers are responsible for creating documentation for technical products and services. This course can be useful for learning some of the technical concepts that are used in autonomous systems.
UX Designer
UX Designer
UX Designers are responsible for creating user interfaces that are easy to use and understand. This course may be useful for understanding the user experience of autonomous systems.
For more career information including salaries, visit:
OpenCourser.com/course/zfmhab/estimatio
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
Estimation.
Covers the fundamental mathematical and algorithmic tools for mobile robot mapping and localization, which provide essential background knowledge for this course.
Focuses on the theoretical foundations of Kalman filtering and valuable reference for deeper understanding of the estimation algorithms used in this course.
This comprehensive textbook covers advanced topics in estimation theory, providing a theoretical foundation for the estimation techniques used in this course.
This textbook covers the fundamentals of robot modeling and control, providing background knowledge for the autonomous flight solution discussed in this course.
This textbook introduces the fundamental principles and algorithms for autonomous mobile robots, providing context for the autonomous flight solution discussed in this course.
Provides a comprehensive treatment of nonlinear estimation theory and algorithms, offering advanced insights into the Extended Kalman Filter used in this course.
Provides a practical approach to Kalman filtering, offering clear explanations and implementation examples for the Extended Kalman Filter used in this course.
This textbook covers advanced topics in optimal state estimation theory and algorithms, extending the concepts discussed in this course.
Introduces Bayesian filtering and smoothing techniques, which complement the Kalman Filter approaches used in this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zfmhab/estimatio
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Time
40320 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Andy Brown
Jake Lussier
Raffaello D'Andrea
Angela Schoellig
Nicholas Roy
Sergei Lupashin
Language
English
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