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You should have intermediate C++ knowledge, and be familiar with calculus, probability, and linear algebra. See detailed requirements.
Process raw lidar data with filtering, segmentation, and clustering to detect other vehicles on the road.Lidar Obstacle Detection
Analyze radar signatures to detect and track objects. Calculate velocity and orientation by correcting for radial velocity distortions, noise, and occlusions.Radar Obstacle Detection
Fuse camera images together with lidar point cloud data. You'll extract object features, classify objects, and project the camera image into three dimensions to fuse with lidar data.Camera and Lidar Fusion
Fuse data from multiple sources using Kalman filters, and build extended and unscented Kalman filters for tracking nonlinear movement.Unscented Kalman Filters
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Head of Curriculum
David Silver leads the Udacity Curriculum Team. Before Udacity, David was a research engineer on the autonomous vehicle team at Ford. He has an MBA from Stanford, and a BSE in Computer Science from Princeton.
Stephen is a Content Developer at Udacity and has worked on the C++ and Self-Driving Car Engineer Nanodegree programs. He started teaching and coding while completing a Ph.D. in mathematics, and has been passionate about engineering education ever since.
Andreas Haja is an engineer, educator and autonomous vehicle enthusiast with a PhD in computer science. Andreas now works as a professor, where he focuses on project-based learning in engineering. During his career with Volkswagen and Bosch he developed camera technology and autonomous vehicle prototypes.
Abdullah holds his M.S from the University of Maryland and is an expert in the field of Radio Frequency Design and Digital Signal processing. After spending several years at Qualcomm, Abdullah joined Metawave, where he now leads Radar development for autonomous driving.
Aaron Brown has a background in electrical engineering, robotics and deep learning. Aaron has worked as a Content Developer and Simulation Engineer at Udacity focusing on developing projects for self-driving cars.
Sensor fusion engineering is one of the most important and exciting areas of robotics.
Sensors like cameras, radar, and lidar help self-driving cars, drones, and all types of robots perceive their environment. Analyzing and fusing this data is fundamental to building an autonomous system.
In this Nanodegree program, you will work with camera images, radar signatures, and lidar point clouds to detect and track vehicles and pedestrians. By graduation, you will have an impressive portfolio of projects to demonstrate your skills to employers.
As a Sensor Fusion Engineer, you'll be equipped to bring value to a wide array of industries and be eligible for many roles.
Your opportunities might include roles such as an:
If you’re interested in learning about lidar, radar, and camera data and how to fuse it together, this program is right for you.
Sensors and sensor data are used in a wide array of applications -- from cell phones to robots and self-driving cars -- giving you a wide array of fields you could enter or advance a career in after this program.
There is no application. This Nanodegree program accepts everyone, regardless of experience and specific background.
To optimize your chances of success in the Sensor Fusion Engineer Nanodegree program, we’ve created a list of prerequisites and recommendations to help prepare you for the program curriculum. You should have the following knowledge:
For aspiring sensor fusion engineers who currently have a limited background in programming or math, we've created the Intro to Self-Driving Cars Nanodegree program to help you prepare. This program teaches C++, linear algebra, calculus, and statistics.
If you have a limited background in programming, we’ve created the C++ Nanodegree program to help you prepare for the coding in this program.
The Sensor Fusion Engineer Nanodegree program is comprised of content and curriculum to support four (4) projects. We estimate that students can complete the program in four (4) months, working 10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Please see the Udacity Program FAQs for policies on enrollment in our programs.
We have few requirements since you will be coding on our virtual environments (“Workspaces”) in the browser. This means you can complete all coursework within our platform, and do not need to install anything on your own machine.
If you choose to complete projects on your local machine, you should install: