Sensor Fusion

Welcome to the Sensor Fusion Engineer Nanodegree Program! In this lesson, you will learn more about the structure of the program and meet the team.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

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.

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.

Introduction to the instructor, and the guest Timo Rehfeld, Mercedes-Benz R&D North America, and the course overview.

Learn about the various levels of autonomy, some typical sensor sets, basics of camera technology, and an introduction into the OpenCV computer vision library.

Learn the collision detection basics, and estimating the TTC with Lidar and Camera.

Learn about the intensity gradient and filtering techniques; extract corners, infer features of an image, and track an object across multiple images.

Learn to improve the tracking process results by combining the Camera and Lidar output

Review Radar functionality, FMCW waveform, Radar Hardware, Schematic and the Radar Equation

Estimate the range and velocity of the target using the FMCW radar

Discuss - Clutter formation and then its removal using CFAR technique. After that

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

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

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.

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!

This optional content is designed to refresh your knowledge of trigonometry and geometry.