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Sebastian Thrun,
Julia Chernushevich,
Karim Chamaa,
and
David Silver
Register for this course and see more details by visiting:
OpenCourser.com/course/sp0xn1/mapping
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What's inside
Syllabus
Introduction to the Mapping and SLAM concepts, as well as the algorithms.
Learn how to map an environment with the Occupancy Grid Mapping algorithm.
Read more
Syllabus
Introduction to the Mapping and SLAM concepts, as well as the algorithms.
Learn how to map an environment with the Occupancy Grid Mapping algorithm.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Intends to provide an experience and setting where learners are creating their own 2D and 3D maps in real time through the lens of a simulated robot
May provide a foundation for future research in robotics, autonomous vehicles, and beyond
Teaches skills and knowledge with potential application in robotics, autonomous vehicles, etc
Build on the prior lessons and knowledge developed in other courses
Assumes a level of prior learning before the learner is able to take this course
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Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Practical slam implementation with ros
According to learners, this course offers a highly practical approach to Simultaneous Localization and Mapping (SLAM), particularly beneficial for those looking to implement SLAM using ROS and C++. Students praise the hands-on labs and projects, especially the effective deployment of RTAB-Map for 2D and 3D mapping. While the course provides comprehensive coverage of key algorithms like Occupancy Grid, FastSLAM, and GraphSLAM, prospective students should be aware it assumes a solid foundation in C++ programming and ROS basics. Some learners found the mathematical derivations could be more detailed, and the pace can be challenging for beginners without the necessary prerequisites.
Generally positive, though some minor inconsistencies noted in presentation.
"I found the instructor's passion shines through."
"The content itself is good, but I found the video quality a bit inconsistent and some explanations felt rushed."
"I found the lectures a bit dry."
Explores various key SLAM algorithms in depth.
"The explanations of GraphSLAM were incredibly clear."
"I found it a solid introduction to SLAM, and the FastSLAM section was well-paced."
"I appreciated the comprehensive coverage of various SLAM techniques, from Occupancy Grids to GraphSLAM."
Focuses on real-world application with strong coding and project components.
"The hands-on labs with RTAB-Map solidified my understanding."
"The coding exercises were challenging but rewarding."
"I found the practical aspects, especially RTAB-Map, to be strong."
"The hands-on projects were outstanding and truly helped reinforce the concepts."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
Some sections could benefit from more detailed mathematical derivations.
"I felt some parts of the mathematical derivations could be explained in more detail."
"I struggled with the amount of assumed knowledge in linear algebra."
"I think some of the theoretical aspects, particularly early on, could benefit from more visual aids or simpler analogies."
"I found the initial learning curve for the theory to be steep."
Can be fast-paced and challenging, especially for those new to the field.
"I found this course extremely difficult. The pace was too fast..."
"I felt the explanations jumped ahead assuming too much."
"I believe this is a course for those ready for a challenge and practical implementation."
Requires existing familiarity with C++ and ROS, and some mathematical background.
"I found it required a good grasp of C++ and basic ROS concepts beforehand."
"Getting started was hard for me without strong prior ROS experience."
"I realized this course is not for total beginners; you need to be comfortable with C++ and ROS."
"I felt the course assumed a solid background in C++ and ROS, as well as some foundational math."
"The course jumped ahead assuming too much prior knowledge."
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 Mapping and SLAM with these
activities:
Review SLAM algorithms
Show steps
Improve your understanding of SLAM fundamentals before starting this course.
Browse courses on
SLAM
Show steps
-
Read research papers on SLAM algorithms.
-
Implement a basic SLAM algorithm in a simulated environment.
Practice ROS navigation
Show steps
Sharpen your ROS navigation skills to better prepare for the course.
Browse courses on
ROS
Show steps
-
Create a ROS workspace and install necessary packages.
-
Write a ROS node to control a simulated robot.
-
Implement obstacle avoidance using ROS navigation stack.
Explore RTAB-Map
Show steps
Gain practical experience with RTAB-Map before using it in the course.
Show steps
-
Install RTAB-Map and its dependencies.
-
Run RTAB-Map on a simulated environment.
-
Visualize and analyze the generated map.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a SLAM study group
Show steps
Connect with peers and collaborate on SLAM-related projects or discussions.
Browse courses on
SLAM
Show steps
-
Find a SLAM study group or create one with fellow students.
-
Meet regularly to discuss course concepts, share knowledge, and work on projects together.
Build a SLAM project proposal
Show steps
Develop a project proposal to apply your SLAM knowledge and skills to a real-world problem.
Browse courses on
SLAM
Show steps
-
Identify a problem that can be solved using SLAM.
-
Research existing SLAM techniques and select an appropriate approach.
-
Design a system architecture and implementation plan.
-
Write a project proposal outlining your plan and expected outcomes.
Attend a SLAM conference
Show steps
Network with experts, learn about the latest SLAM research, and gain insights from industry professionals.
Browse courses on
SLAM
Show steps
-
Identify and register for a relevant SLAM conference.
-
Attend technical sessions, workshops, and networking events.
-
Connect with researchers, engineers, and industry leaders.
Contribute to a SLAM open-source project
Show steps
Gain hands-on experience by contributing to the development of open-source SLAM software.
Browse courses on
SLAM
Show steps
-
Find an open-source SLAM project that aligns with your interests.
-
Familiarize yourself with the project's codebase and documentation.
-
Identify an area where you can contribute.
-
Submit a bug report, feature request, or code contribution.
Review SLAM algorithms
Show steps
Improve your understanding of SLAM fundamentals before starting this course.
Browse courses on
SLAM
Show steps
Show steps
Show steps
- Read research papers on SLAM algorithms.
- Implement a basic SLAM algorithm in a simulated environment.
Practice ROS navigation
Show steps
Sharpen your ROS navigation skills to better prepare for the course.
Browse courses on
ROS
Show steps
Show steps
Show steps
- Create a ROS workspace and install necessary packages.
- Write a ROS node to control a simulated robot.
- Implement obstacle avoidance using ROS navigation stack.
Explore RTAB-Map
Show steps
Gain practical experience with RTAB-Map before using it in the course.
Show steps
Show steps
Show steps
- Install RTAB-Map and its dependencies.
- Run RTAB-Map on a simulated environment.
- Visualize and analyze the generated map.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a SLAM study group
Show steps
Connect with peers and collaborate on SLAM-related projects or discussions.
Browse courses on
SLAM
Show steps
Show steps
Show steps
- Find a SLAM study group or create one with fellow students.
- Meet regularly to discuss course concepts, share knowledge, and work on projects together.
Build a SLAM project proposal
Show steps
Develop a project proposal to apply your SLAM knowledge and skills to a real-world problem.
Browse courses on
SLAM
Show steps
Show steps
Show steps
- Identify a problem that can be solved using SLAM.
- Research existing SLAM techniques and select an appropriate approach.
- Design a system architecture and implementation plan.
- Write a project proposal outlining your plan and expected outcomes.
Attend a SLAM conference
Show steps
Network with experts, learn about the latest SLAM research, and gain insights from industry professionals.
Browse courses on
SLAM
Show steps
Show steps
Show steps
- Identify and register for a relevant SLAM conference.
- Attend technical sessions, workshops, and networking events.
- Connect with researchers, engineers, and industry leaders.
Contribute to a SLAM open-source project
Show steps
Gain hands-on experience by contributing to the development of open-source SLAM software.
Browse courses on
SLAM
Show steps
Show steps
Show steps
- Find an open-source SLAM project that aligns with your interests.
- Familiarize yourself with the project's codebase and documentation.
- Identify an area where you can contribute.
- Submit a bug report, feature request, or code contribution.
Career center
Learners who complete Mapping and SLAM will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision algorithms and systems. They work on a variety of applications, including image processing, object recognition, and motion tracking. This course would be helpful for Computer Vision Engineers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing computer vision systems that can navigate and map their environment.
Mobile Robotics Engineer
Mobile Robotics Engineer
Mobile Robotics Engineers design, build, and maintain mobile robots. They work on a variety of applications, including autonomous vehicles, drones, and service robots. This course would be helpful for Mobile Robotics Engineers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing mobile robots that can navigate and map their environment.
Robotics Software Engineer
Robotics Software Engineer
Robotics Software Engineers develop and maintain software for robots. They work on a variety of applications, including autonomous vehicles, drones, and service robots. This course would be helpful for Robotics Software Engineers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing robots that can navigate and map their environment.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, build, and maintain autonomous vehicles. They work on a variety of applications, including self-driving cars, trucks, and buses. This course would be helpful for Autonomous Vehicle Engineers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing autonomous vehicles that can navigate and map their environment.
Drone Engineer
Drone Engineer
Drone Engineers design, build, and maintain drones. They work on a variety of applications, including aerial photography, surveillance, and delivery. This course would be helpful for Drone Engineers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing drones that can navigate and map their environment.
Geomatics Engineer
Geomatics Engineer
Geomatics Engineers use geospatial data to solve problems in a variety of fields, including surveying, mapping, and construction. This course would be helpful for Geomatics Engineers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing geospatial data collection and processing systems.
Cartographer
Cartographer
Cartographers create maps and other geospatial data products. They work on a variety of applications, including navigation, land use planning, and environmental management. This course would be helpful for Cartographers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing geospatial data collection and processing systems.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts use geospatial data to analyze and solve problems in a variety of fields, including environmental science, public health, and urban planning. This course would be helpful for Geospatial Analysts because it provides a foundation in mapping and SLAM algorithms, which are essential for developing geospatial data collection and processing systems.
Robotics Researcher
Robotics Researcher
Robotics Researchers develop new technologies and applications for robots. This course would be helpful for Robotics Researchers because it provides a foundation in mapping and SLAM algorithms, which are essential for developing autonomous robots.
Computer Science Professor
Computer Science Professor
Computer Science Professors teach and conduct research in computer science. This course would be helpful for Computer Science Professors because it provides a foundation in mapping and SLAM algorithms, which are essential for teaching and researching in the field of robotics.
Civil Engineer
Civil Engineer
Civil Engineers design and build infrastructure, such as roads, bridges, and buildings. This course may be helpful for Civil Engineers because it provides a foundation in mapping and SLAM algorithms, which could be useful for developing infrastructure monitoring and management systems.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design and build machines and other mechanical systems. This course may be helpful for Mechanical Engineers because it provides a foundation in mapping and SLAM algorithms, which could be useful for developing autonomous machines and systems.
Electrical Engineer
Electrical Engineer
Electrical Engineers design and build electrical systems and components. This course may be helpful for Electrical Engineers because it provides a foundation in mapping and SLAM algorithms, which could be useful for developing autonomous electrical systems and components.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new algorithms and techniques for artificial intelligence. This course may be helpful for Artificial Intelligence Researchers because it provides a foundation in mapping and SLAM algorithms, which could be useful for developing autonomous systems and agents.
Environmental Engineer
Environmental Engineer
Environmental Engineers design and implement solutions to environmental problems. They work on a variety of applications, including water treatment, air pollution control, and waste management. This course may be helpful for Environmental Engineers because it provides a foundation in mapping and SLAM algorithms, which could be useful for developing environmental monitoring and remediation systems.
For more career information including salaries, visit:
OpenCourser.com/course/sp0xn1/mapping
Reading list
We've selected 11 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
Mapping and SLAM.
This classic textbook covers foundational concepts in probabilistic robotics, including localization, mapping, and SLAM. Essential for building a strong theoretical understanding of the core principles and algorithms used in SLAM.
Provides practical implementations of robotics, vision, and control algorithms in MATLAB. Useful as a reference for coding and implementing SLAM algorithms.
Provides a comprehensive overview of SLAM algorithms and techniques, with a focus on mobile robots. It valuable resource for anyone interested in learning more about the practical aspects of SLAM.
This textbook provides a comprehensive foundation in probabilistic graphical models, which are essential for understanding the probabilistic underpinnings of SLAM algorithms.
Provides a comprehensive overview of autonomous mobile robotics, with chapters covering SLAM and other navigation techniques.
Provides a solid foundation in machine learning, which is essential for understanding SLAM algorithms.
Provides a comprehensive introduction to the field of autonomous mobile robots, covering topics such as SLAM, path planning, and control. It valuable reference for anyone interested in learning more about the theoretical foundations of SLAM.
Provides a solid foundation in data structures and algorithms, which are essential for understanding and implementing SLAM algorithms efficiently.
Save
Provides an overview of deep learning, which promising new approach to SLAM.
Provides a comprehensive introduction to the field of robot modeling and control, covering topics such as kinematics, dynamics, and control theory. It valuable reference for anyone interested in learning more about the theoretical foundations of SLAM.
Save
Provides a comprehensive overview of computer vision, which is essential for understanding SLAM algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sp0xn1/mapping
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