Sensor Fusion

Welcome to the Sensor Fusion Engineer Nanodegree Program

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.

Introduction to Sensor Fusion

Get Help with Your Account

What to do if you have questions about your account or general questions about the program.

Getting Help

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

Introduction to Lidar and Point Clouds

Learn about lidar and point clouds. Use a simulation highway environment to explore lidar sensing and generate point clouds.

Point Cloud Segmentation

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.

Clustering Obstacles

Perform Euclidean clustering, and learn how to build KD-Trees to use them to do efficient nearest neighbor search for clustering.

Working with Real PCD

Take what you have learned in the previous lessons and apply it to real pcd being played back in a video.

Lidar Obstacle Detection Project

In this lesson, students will submit the project that they have developed over the previous lessons.

Introduction

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

Autonomous Vehicles and Computer Vision

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

Engineering a Collision Detection System

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

Tracking Image Features

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

Camera Based 2D Feature Tracking

Combining Camera and Lidar

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

Track an Object in 3D Space

Final Thoughts from Timo

Introduction

Radar Principles

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

Range-Doppler Estimation

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

Clutter, CFAR, AoA, and Clustering

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

Radar Target Generation and Detection

Introduction and Sensors

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

Kalman Filters

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

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.

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!

Unscented Kalman Filter Highway Project

In this lesson, students will submit the project that they have developed over the previous lessons.

Geometry and Trigonometry Refresher

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

Industry Research

You're building your online presence. Now learn how to share your story, understand the tech landscape better, and meet industry professionals.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.

Autonomous Systems Interview Practice

Start off with some tips on interviewing for an autonomous systems role, then watch how candidates approach their interview questions. Finish off by practicing some questions of your own!

Kalman Filter in MATLAB