We may earn an affiliate commission when you visit our partners.
Lidar Obstacle Detection
David Silver,
Stephen Welch,
Abdullah Zaidi,
Andreas Haja,
and
Aaron Brown
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
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
Coming soon
We're preparing activities for
Lidar Obstacle Detection.
These are activities you can do either before, during, or after a course.
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
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
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
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser