About this Course

This course is a part of the Self-Driving Car Engineer Nanodegree Program.

Vehicles use many different sensors to understand the environment. Sensor fusion uses different types of Kalman filters - mathematical algorithms - to combine data from these sensors and develop a consistent understanding of the world.

Skill Level
Advanced
Included in Course
  • Rich Learning Content

  • Interactive Quizzes

  • Taught by Industry Pros

  • Real World Projects

  • Student Support Community

  • Personalized Career Support

Join the Path to Greatness

This course is part of a Nanodegree Program. It is a step towards a new career in Self-Driving Car Engineer.

Nanodegree Course

Self-Driving Car Engineer - Sensor Fusion

by Mercedes

Enhance your skill set and boost your hirability through innovative, independent learning.

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What You Will Learn

Lesson 1

Introduction and Sensors

  • Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.
Lesson 1

Introduction and Sensors

  • Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.
Lesson 2

Kalman Filters

  • Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
Lesson 2

Kalman Filters

  • Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
Lesson 3

C++ Checkpoint

  • Are you ready to build Kalman Filters with C++? Take these quizzes to find out.
Lesson 3

C++ Checkpoint

  • Are you ready to build Kalman Filters with C++? Take these quizzes to find out.
Lesson 4

Lidar and Radar Fusion with Kalman Filters in C++

  • In this lesson, you'll build a Kalman Filter in C++ that's capable of handling data from multiple sources. Why C++? Its performance enables the application of object tracking with a Kalman Filter in real-time.
Lesson 4

Lidar and Radar Fusion with Kalman Filters in C++

  • In this lesson, you'll build a Kalman Filter in C++ that's capable of handling data from multiple sources. Why C++? Its performance enables the application of object tracking with a Kalman Filter in real-time.
Lesson 5

Extended Kalman Filter Project

  • In this project, you'll apply everything you've learned so far about Sensor Fusion by implementing an Extended Kalman Filter in C++!
Lesson 5

Extended Kalman Filter Project

  • In this project, you'll apply everything you've learned so far about Sensor Fusion by implementing an Extended Kalman Filter in C++!
Lesson 6

Unscented Kalman Filters

  • While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!
Lesson 6

Unscented Kalman Filters

  • While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!
Lesson 7

Unscented Kalman Filter Project

  • Put your skills to the test! Use C++ to code an Unscented Kalman Filter capable of tracking non-linear motion.
Lesson 7

Unscented Kalman Filter Project

  • Put your skills to the test! Use C++ to code an Unscented Kalman Filter capable of tracking non-linear motion.

Prerequisites and Requirements

C++, Calculus, Linear Algebra

See the Technology Requirements for using Udacity.

Why Take This Course

By the end of this course, you will be able to program Kalman filters to fuse together radar and lidar data to track an object. You will be able to build extended Kalman filters and unscented Kalman filters to fuse together nonlinear data.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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