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David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
Besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability, using sensor fusion. You will learn about the lidar sensor, different lidar types, and relevant criteria for sensor...
Read more
Besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability, using sensor fusion. You will learn about the lidar sensor, different lidar types, and relevant criteria for sensor...
Read more
Besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability, using sensor fusion. You will learn about the lidar sensor, different lidar types, and relevant criteria for sensor selection. Also, you will learn how to detect objects in a 3D lidar point cloud using a deep-learning approach, and then evaluate detection performance using a set of metrics.
In the second half of the course, you will learn how to fuse camera and lidar detections and track objects over time with an Extended Kalman Filter. You will get hands-on experience with multi-target tracking, where you will initialize, update and delete tracks, assign measurements to tracks with data association techniques, and manage several tracks simultaneously.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Get started with sensor fusion and perception, why they are important, and the history of their development in self-driving cars.
Learn about the lidar sensor, capable of capturing important 3D data in point clouds.
Read more
Syllabus
Get started with sensor fusion and perception, why they are important, and the history of their development in self-driving cars.
Learn about the lidar sensor, capable of capturing important 3D data in point clouds.
Read more
Detect objects from the 3D data coming in from a lidar sensor.
Use the Waymo dataset to detect 3D objects in the surrounding environment.
Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
Build an Extended Kalman Filter that's capable of handling data from multiple sources.
Get your tracking skills ready for the real world by learning how to track multiple targets simultaneously.
Use the Waymo dataset, along with sensor fusion, to track multiple 3D objects in the surrounding environment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops sensor fusion and perception, which are core skills for autonomous driving
Examines lidar sensors, which are highly relevant to autonomous vehicle development
Taught by David Silver, Thomas Hossler, Antje Muntzinger, Andreas Haja, Aaron Brown, Munir Jojo Verge, and Mathilde Badoual, who are recognized for their work in autonomous driving
Uses the Waymo dataset, which is highly relevant to autonomous vehicle development
Offers hands-on labs and interactive materials
Save this course
Save Sensor fusion to your list so you can find it easily later:
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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 Sensor fusion with these
activities:
Attend industry conferences and meetups
Show steps
Expand your professional network, learn about industry trends, and connect with potential collaborators by attending relevant industry events.
Show steps
-
Research upcoming conferences and meetups related to self-driving cars
-
Register for events that align with your interests
-
Prepare an elevator pitch to introduce yourself and your work
Review the lidar sensor basics
Show steps
Brush up on the basics of the lidar sensor to prepare for the course content.
Show steps
-
Watch a video tutorial on lidar sensors.
-
Read an article about lidar technology.
-
Complete an online quiz on lidar systems.
Develop a comprehensive study guide
Show steps
Create a valuable resource to enhance your understanding of course content by compiling a comprehensive study guide that includes key concepts, summaries, and practice questions.
Show steps
-
Review course materials, including lectures, notes, and readings
-
Organize and summarize important concepts and definitions
-
Include practice questions and exercises to test your understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Set up the Udacity workspace
Show steps
Set up your Udacity workspace to ensure you can access all the necessary tools and resources for the course.
Show steps
-
Create a Udacity account.
-
Install the required software and libraries.
-
Import the course materials into your workspace.
Practice detecting objects in a lidar point cloud
Show steps
Develop skills in detecting objects in a lidar point cloud, a key task in self-driving car perception.
Browse courses on
Object Detection
Show steps
-
Download a lidar dataset.
-
Use a deep-learning library to detect objects in the dataset.
-
Evaluate the performance of your object detector.
Collaborative problem-solving sessions
Show steps
Connect with fellow learners to discuss course concepts, troubleshoot problems, and share insights, fostering a collaborative learning environment.
Show steps
-
Join a study group or create one with peers
-
Set regular meeting times and establish clear goals for each session
-
Take turns presenting and discussing course material
Implement an Extended Kalman Filter for object tracking
Show steps
Gain hands-on experience implementing an Extended Kalman Filter for object tracking, a fundamental technique in self-driving car perception.
Browse courses on
Extended Kalman Filter
Show steps
-
Review the Kalman Filter algorithm.
-
Implement the Extended Kalman Filter for object tracking.
-
Test your implementation on a real-world dataset.
Attend industry conferences and meetups
Show steps
Expand your professional network, learn about industry trends, and connect with potential collaborators by attending relevant industry events.
Show steps
Show steps
Show steps
- Research upcoming conferences and meetups related to self-driving cars
- Register for events that align with your interests
- Prepare an elevator pitch to introduce yourself and your work
Review the lidar sensor basics
Show steps
Brush up on the basics of the lidar sensor to prepare for the course content.
Show steps
Show steps
Show steps
- Watch a video tutorial on lidar sensors.
- Read an article about lidar technology.
- Complete an online quiz on lidar systems.
Develop a comprehensive study guide
Show steps
Create a valuable resource to enhance your understanding of course content by compiling a comprehensive study guide that includes key concepts, summaries, and practice questions.
Show steps
Show steps
Show steps
- Review course materials, including lectures, notes, and readings
- Organize and summarize important concepts and definitions
- Include practice questions and exercises to test your understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Set up the Udacity workspace
Show steps
Set up your Udacity workspace to ensure you can access all the necessary tools and resources for the course.
Show steps
Show steps
Show steps
- Create a Udacity account.
- Install the required software and libraries.
- Import the course materials into your workspace.
Practice detecting objects in a lidar point cloud
Show steps
Develop skills in detecting objects in a lidar point cloud, a key task in self-driving car perception.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Download a lidar dataset.
- Use a deep-learning library to detect objects in the dataset.
- Evaluate the performance of your object detector.
Collaborative problem-solving sessions
Show steps
Connect with fellow learners to discuss course concepts, troubleshoot problems, and share insights, fostering a collaborative learning environment.
Show steps
Show steps
Show steps
- Join a study group or create one with peers
- Set regular meeting times and establish clear goals for each session
- Take turns presenting and discussing course material
Implement an Extended Kalman Filter for object tracking
Show steps
Gain hands-on experience implementing an Extended Kalman Filter for object tracking, a fundamental technique in self-driving car perception.
Browse courses on
Extended Kalman Filter
Show steps
Show steps
Show steps
- Review the Kalman Filter algorithm.
- Implement the Extended Kalman Filter for object tracking.
- Test your implementation on a real-world dataset.
Career center
Learners who complete Sensor fusion will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to extract meaningful insights, and sensor fusion and perception play a vital role in this process. Those in this role need to fully understand the concepts behind sensor fusion and perception to successfully perform in their jobs. This course can help Data Scientists build a foundation in sensor fusion and perception, a skill which will enable them to better analyze, interpret, and extract meaning from data.
Perception Engineer
Perception Engineer
Perception Engineers develop and maintain software and systems that allow self-driving cars to perceive and understand their surroundings using sensor fusion. This course can help Perception Engineers hone their skills in sensor fusion and perception, enabling them to improve the accuracy, reliability, and safety of self-driving cars.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers apply computer vision techniques to develop software and systems that allow self-driving cars to recognize and understand objects in their surroundings using sensor fusion. This course can help Computer Vision Engineers enhance their skills in sensor fusion and perception, enabling them to create more sophisticated and accurate vision systems for self-driving cars.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots and robotic systems using sensor fusion. This course can help Robotics Engineers build a foundation in sensor fusion and perception, enabling them to develop more advanced and autonomous robots.
Mechatronics Engineer
Mechatronics Engineer
Mechatronics Engineers combine mechanical, electrical, and computer engineering to design and develop products and systems using sensor fusion. This course can help Mechatronics Engineers enhance their skills in sensor fusion and perception, enabling them to create more innovative and efficient products and systems.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test autonomous vehicles using sensor fusion. This course can help Autonomous Vehicle Engineers build a foundation in sensor fusion and perception, enabling them to develop safer and more reliable autonomous vehicles.
Automotive Engineer
Automotive Engineer
Automotive Engineers design, develop, and test vehicles and automotive systems using sensor fusion. This course can help Automotive Engineers enhance their skills in sensor fusion and perception, enabling them to develop more advanced and efficient vehicles.
Software Engineer
Software Engineer
Software Engineers develop and maintain software and systems that use sensor fusion. This course can help Software Engineers build a foundation in sensor fusion and perception, enabling them to develop more robust and reliable software.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical and electronic systems that use sensor fusion. This course can help Electrical Engineers build a foundation in sensor fusion and perception, enabling them to develop more advanced and efficient systems.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design, develop, and maintain mechanical systems that use sensor fusion. This course can help Mechanical Engineers build a foundation in sensor fusion and perception, enabling them to develop more innovative and effective systems.
Aerospace Engineer
Aerospace Engineer
Aerospace Engineers design, develop, and maintain aerospace systems that use sensor fusion. This course can help Aerospace Engineers build a foundation in sensor fusion and perception, enabling them to develop safer and more efficient aerospace systems.
Materials Engineer
Materials Engineer
Materials Engineers develop and test materials used in sensor fusion systems. This course can help Materials Engineers build a foundation in sensor fusion and perception, enabling them to develop new and innovative materials for use in sensor fusion systems.
Chemical Engineer
Chemical Engineer
Chemical Engineers design and develop chemical processes that use sensor fusion. This course can help Chemical Engineers build a foundation in sensor fusion and perception, enabling them to develop more efficient and sustainable chemical processes.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers design and develop medical devices and systems that use sensor fusion. This course can help Biomedical Engineers build a foundation in sensor fusion and perception, enabling them to develop more effective and patient-centric medical devices and systems.
Environmental Engineer
Environmental Engineer
Environmental Engineers design and develop systems to protect the environment using sensor fusion. This course can help Environmental Engineers build a foundation in sensor fusion and perception, enabling them to develop more effective and efficient environmental protection systems.
For more career information including salaries, visit:
OpenCourser.com/course/0ske4v/sensor
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
Sensor fusion.
Save
Provides a comprehensive overview of computer vision algorithms and techniques. It valuable resource for anyone interested in learning about computer vision, including sensor fusion and perception systems.
Provides a comprehensive overview of deep learning for computer vision tasks, including object detection and tracking. It valuable resource for anyone interested in learning about deep learning and its applications to computer vision.
Provides a comprehensive overview of Kalman filtering algorithms and techniques. It valuable resource for anyone interested in learning about Kalman filtering, including sensor fusion and perception systems.
Provides a comprehensive overview of robot perception, including sensor fusion and perception systems. It valuable resource for anyone interested in learning about robot perception.
Save
Provides a comprehensive overview of SLAM algorithms and techniques. It valuable resource for anyone interested in learning about SLAM, including sensor fusion and perception systems.
Provides a comprehensive overview of state estimation algorithms and techniques. It valuable resource for anyone interested in learning about state estimation, including sensor fusion and perception systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0ske4v/sensor
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Time
40320 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Thomas Hossler
Antje Muntzinger
Andreas Haja
Aaron Brown
Munir Jojo Verge
Mathilde Badoual
Language
English
Good to know
Develops sensor fusion and perception, which are core skills for autonomous driving
Examines lidar sensors, which are highly relevant to autonomous vehicle development
Taught by David Silver, Thomas Hossler, Antje Muntzinger, Andreas Haja, Aaron Brown, Munir Jojo Verge, and Mathilde Badoual, who are recognized for their work in autonomous driving
Uses the Waymo dataset, which is highly relevant to autonomous vehicle development
Offers hands-on labs and interactive materials
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