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David Silver,
Stephen Welch,
Abdullah Zaidi,
Andreas Haja,
and
Aaron Brown
Register for this course and see more details by visiting:
OpenCourser.com/course/bwv4r4/lidar
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What's inside
Syllabus
Learn about lidar and point clouds. Use a simulation highway environment to explore lidar sensing and generate point clouds.
In this lesson, you will be using Ransac with a plane model to segment point cloud data and separate it into points that are part of the road and points that are not.
Read more
Syllabus
Learn about lidar and point clouds. Use a simulation highway environment to explore lidar sensing and generate point clouds.
In this lesson, you will be using Ransac with a plane model to segment point cloud data and separate it into points that are part of the road and points that are not.
Read more
Perform Euclidean clustering, and learn how to build KD-Trees to use them to do efficient nearest neighbor search for clustering.
Take what you have learned in the previous lessons and apply it to real pcd being played back in a video.
In this lesson, students will submit the project that they have developed over the previous lessons.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops road segmentation with Lidar and point cloud data, which are essential skills for self-driving car engineers
Taught by David Silver, Stephen Welch, Abdullah Zaidi, Andreas Haja, and Aaron Brown, who are renowned experts in the field of self-driving cars
Provides hands-on practice with real-world Lidar data, allowing learners to apply their knowledge to practical scenarios
Builds a strong foundation in Lidar and point cloud data analysis, which is crucial for professionals working on self-driving cars
Instructs learners on using RANSAC and Euclidean clustering, essential techniques for point cloud processing
Save this course
Save Lidar Obstacle Detection 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 Lidar Obstacle Detection with these
activities:
Review lidar concepts
Show steps
Refreshes your knowledge of lidar concepts, making it easier to understand the course material.
Browse courses on
LiDAR
Show steps
-
Read through your notes from previous courses or textbooks on lidar technology.
-
Review online resources, such as articles and videos, to reinforce your understanding of lidar principles.
-
Complete practice problems or quizzes to test your understanding of lidar concepts.
Tutorial: Euclidean Clustering
Show steps
Provides hands-on practice with Euclidean clustering, which is covered in the course.
Show steps
-
Find a tutorial on Euclidean clustering, such as the one on the OpenCV website.
-
Follow the tutorial step-by-step, implementing the algorithm in your preferred programming language.
-
Test your implementation on different point cloud datasets to see how it performs.
Peer discussion: Lidar applications
Show steps
Fosters collaboration and knowledge sharing among peers by discussing lidar applications.
Show steps
-
Join or form a study group with classmates.
-
Choose a topic related to lidar applications and research it thoroughly.
-
Prepare a brief presentation or talking points to share your findings and start the discussion.
-
Facilitate the discussion, encouraging active participation from all members.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice RANSAC for plane segmentation
Show steps
Reinforces your understanding of RANSAC and improves your ability to apply it to point cloud processing tasks.
Show steps
-
Find online resources or exercises that provide practice problems for RANSAC.
-
Implement RANSAC in your preferred programming language and test it on various point cloud datasets.
-
Experiment with different parameters and thresholds to optimize the performance of your RANSAC implementation.
Build a simple lidar processing application
Show steps
Allows you to apply your learning by creating a practical application.
Show steps
-
Choose a specific lidar processing task, such as object detection or point cloud segmentation.
-
Design and implement your application using appropriate algorithms and libraries.
-
Test your application on different datasets and evaluate its performance.
-
Document your application, including the algorithms used and the results obtained.
Create a presentation on point cloud segmentation
Show steps
Encourages you to synthesize your understanding of point cloud segmentation by creating a presentation.
Show steps
-
Gather information on point cloud segmentation from the course materials and additional sources.
-
Organize your information into a logical flow and create slides with clear and concise content.
-
Practice your presentation skills to deliver a clear and engaging presentation.
-
Share your presentation with others to receive feedback and improve your understanding.
Participate in a point cloud processing competition
Show steps
Provides a challenging and motivating way to test your skills and learn from others.
Show steps
-
Identify relevant competitions, such as the IEEE Point Cloud Processing Contest.
-
Form or join a team and develop a strategy for tackling the competition.
-
Implement your algorithms and optimize your code for efficiency and accuracy.
-
Submit your solution and analyze the results to identify areas for improvement.
Review lidar concepts
Show steps
Refreshes your knowledge of lidar concepts, making it easier to understand the course material.
Browse courses on
LiDAR
Show steps
Show steps
Show steps
- Read through your notes from previous courses or textbooks on lidar technology.
- Review online resources, such as articles and videos, to reinforce your understanding of lidar principles.
- Complete practice problems or quizzes to test your understanding of lidar concepts.
Tutorial: Euclidean Clustering
Show steps
Provides hands-on practice with Euclidean clustering, which is covered in the course.
Show steps
Show steps
Show steps
- Find a tutorial on Euclidean clustering, such as the one on the OpenCV website.
- Follow the tutorial step-by-step, implementing the algorithm in your preferred programming language.
- Test your implementation on different point cloud datasets to see how it performs.
Peer discussion: Lidar applications
Show steps
Fosters collaboration and knowledge sharing among peers by discussing lidar applications.
Show steps
Show steps
Show steps
- Join or form a study group with classmates.
- Choose a topic related to lidar applications and research it thoroughly.
- Prepare a brief presentation or talking points to share your findings and start the discussion.
- Facilitate the discussion, encouraging active participation from all members.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice RANSAC for plane segmentation
Show steps
Reinforces your understanding of RANSAC and improves your ability to apply it to point cloud processing tasks.
Show steps
Show steps
Show steps
- Find online resources or exercises that provide practice problems for RANSAC.
- Implement RANSAC in your preferred programming language and test it on various point cloud datasets.
- Experiment with different parameters and thresholds to optimize the performance of your RANSAC implementation.
Build a simple lidar processing application
Show steps
Allows you to apply your learning by creating a practical application.
Show steps
Show steps
Show steps
- Choose a specific lidar processing task, such as object detection or point cloud segmentation.
- Design and implement your application using appropriate algorithms and libraries.
- Test your application on different datasets and evaluate its performance.
- Document your application, including the algorithms used and the results obtained.
Create a presentation on point cloud segmentation
Show steps
Encourages you to synthesize your understanding of point cloud segmentation by creating a presentation.
Show steps
Show steps
Show steps
- Gather information on point cloud segmentation from the course materials and additional sources.
- Organize your information into a logical flow and create slides with clear and concise content.
- Practice your presentation skills to deliver a clear and engaging presentation.
- Share your presentation with others to receive feedback and improve your understanding.
Participate in a point cloud processing competition
Show steps
Provides a challenging and motivating way to test your skills and learn from others.
Show steps
Show steps
Show steps
- Identify relevant competitions, such as the IEEE Point Cloud Processing Contest.
- Form or join a team and develop a strategy for tackling the competition.
- Implement your algorithms and optimize your code for efficiency and accuracy.
- Submit your solution and analyze the results to identify areas for improvement.
Career center
Learners who complete Lidar Obstacle Detection will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer will apply their knowledge of computer vision, deep learning, and machine learning to build systems that recognize objects within images and videos. Lidar Obstacle Detection may help build a foundation for such a role since it goes over lidar and point clouds, which are often used in computer vision for identifying objects in 3D space. The course also introduces segmentation, clustering, and nearest neighbor search, all of which are critical in the field of computer vision.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build machine learning systems to solve business problems. Lidar Obstacle Detection may help build a foundation for such a role, as it goes over supervised and unsupervised machine learning models for point clouds.
Data Scientist
Data Scientist
Data Scientists analyze and interpret data in order to provide insights and solutions to business problems. Lidar Obstacle Detection may help build a foundation for such a role, as it goes over working with laser scanning data and building algorithms that can process and interpret it.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. Lidar Obstacle Detection may be useful for those who wish to work with robots that navigate autonomously, as the course provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Software Engineer
Software Engineer
Software Engineers will apply their knowledge of computer programming to build software applications. Lidar Obstacle Detection may be useful for those who wish to work on software that involves lidar or point clouds, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design and maintain mechanical systems. Lidar Obstacle Detection may be useful for those who wish to work on mechanical systems that involve lidar or point clouds, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Electrical Engineer
Electrical Engineer
Electrical Engineers design and maintain electrical systems. Lidar Obstacle Detection may be useful for those who wish to work on electrical systems that involve lidar or point clouds, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Civil Engineer
Civil Engineer
Civil Engineers design and maintain civil infrastructure. Lidar Obstacle Detection may be useful for those who wish to work on civil infrastructure that involves lidar or point clouds, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Geographer
Geographer
Geographers study the Earth's surface and its human and natural environments. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their geographic work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Environmental Scientist
Environmental Scientist
Environmental Scientists study the environment and its interactions with humans. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their environmental science work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Archaeologist
Archaeologist
Archaeologists study human history and prehistory. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their archaeological work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Hydrologist
Hydrologist
Hydrologists study water and its movement. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their hydrological work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Geologist
Geologist
Geologists study the Earth's physical structure and history. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their geological work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Anthropologist
Anthropologist
Anthropologists study human beings and their societies. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their anthropological work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
Geophysicist
Geophysicist
Geophysicists study the Earth's physical properties and processes. Lidar Obstacle Detection may be useful for those who wish to use lidar or point clouds in their geophysical work, as it provides opportunities to interact with virtual environments where lidar is responsible for obstacle detection.
For more career information including salaries, visit:
OpenCourser.com/course/bwv4r4/lidar
Reading list
We've selected six 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
Lidar Obstacle Detection.
Provides a comprehensive overview of the control of robot manipulators. It is suitable for advanced students and researchers.
Save
An in-depth exploration of the fundamental concepts, algorithms, and applications of computer vision. Includes coverage of point cloud processing, a key element in lidar obstacle detection.
Save
Provides a comprehensive overview of robotics, including topics such as kinematics, dynamics, and control. It is suitable for advanced students and researchers.
Explores advanced statistical learning techniques, including the least absolute shrinkage and selection operator (LASSO) and its generalizations. While not directly covering lidar-based obstacle detection, it provides valuable background for understanding machine learning algorithms used in such systems.
Comprehensive textbook that provides a solid foundation in the kinematics, dynamics, and control of robots. While focused on general robotics, the book includes chapters on robot sensing, which is relevant to lidar-based obstacle detection.
Save
A rigorous textbook on convex optimization, providing a theoretical basis for algorithms used in lidar-based obstacle detection. Covers topics such as linear programming, quadratic programming, and semidefinite programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bwv4r4/lidar
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Similar courses
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Time
30240 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Stephen Welch
Abdullah Zaidi
Andreas Haja
Aaron Brown
Language
English
Good to know
Develops road segmentation with Lidar and point cloud data, which are essential skills for self-driving car engineers
Taught by David Silver, Stephen Welch, Abdullah Zaidi, Andreas Haja, and Aaron Brown, who are renowned experts in the field of self-driving cars
Provides hands-on practice with real-world Lidar data, allowing learners to apply their knowledge to practical scenarios
Builds a strong foundation in Lidar and point cloud data analysis, which is crucial for professionals working on self-driving cars
Instructs learners on using RANSAC and Euclidean clustering, essential techniques for point cloud processing
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