Sensor Fusion v5.0.0

Sensor Fusion

udaicty

Nanodegree key: nd313 需要项目参考答案跟一对一VIP服务请扫描上方二维码

Version: 5.0.0

Locale: en-us

This course trains the learner to be a sensor fusion engineer focusing on Lidar, Radar technologies.

Content

Part 01 : Welcome

Module 01: Orientation
- Lesson 01: 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.
- Lesson 02: Introduction to Sensor Fusion
Module 02: Career Support
- Lesson 01: Get Help with Your Account
  
  What to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 02: Knowledge, Community, and Careers
  
  You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Part 02 : Lidar Obstacle Detection

Module 01: Lessons
- Lesson 01: 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.
- Lesson 02: 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.
- Lesson 03: Clustering Obstacles
  
  Perform Euclidean clustering, and learn how to build KD-Trees to use them to do efficient nearest neighbor search for clustering.
- Lesson 04: 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.
- Lesson 05: Lidar Obstacle Detection Project
  
  In this lesson, students will submit the project that they have developed over the previous lessons.
  
  Project Description - Lidar Obstacle Detection
  
  Project Rubric - Lidar Obstacle Detection
  - Concept 01: Submitting Your Project

Part 03 : Camera

Module 01: Lessons

Part 04 : Radar

Module 01: Lessons
- Lesson 01: Introduction
- Lesson 02: Radar Principles
  
  Review Radar functionality, FMCW waveform, Radar Hardware, Schematic and the Radar Equation
- Lesson 03: Range-Doppler Estimation
  
  Estimate the range and velocity of the target using the FMCW radar
- Lesson 04: Clutter, CFAR, AoA
  
  Discuss - Clutter formation and then its removal using CFAR technique. After that
- Lesson 05: Clustering and Tracking
- Lesson 06: Radar Target Generation and Detection
  Project Description - Radar Target Generation and Detection
  
  Project Rubric - Radar Target Generation and Detection

Part 05 : Kalman Filters

Module 01: Lessons
- Lesson 01: Introduction and Sensors
  
  Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.
- Lesson 02: Kalman Filters
  
  Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
- Lesson 03: 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 04: 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 05: Unscented Kalman Filter Highway Project
  
  In this lesson, students will submit the project that they have developed over the previous lessons.
  
  Project Description - Unscented Kalman Filter Highway Project
  
  Project Rubric - Unscented Kalman Filter Highway Project
  - Concept 01: Submitting Your Project
  - Concept 02: Additional Project Details

Part 06 : Career Services

These Career Services will ensure you make meaningful connections with industry professionals to accelerate your career growth - whether looking for a job or opportunities to collaborate with your peers. Unlike your Nanodegree projects, you do not need to meet specifications on these Services to progress in your program. Submit these Career Services once, and get honest, personalized feedback and next steps from Udacity Career Coaches!

Module 01: Career Services
- Lesson 01: Industry Research
  
  You're building your online presence. Now learn how to share your story, understand the tech landscape better, and meet industry professionals.
- Lesson 02: 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.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile
- Lesson 03: 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.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile

**Part 07 (Elective) :** Autonomous Systems Interview

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!

Module 01: Autonomous Systems Interview
- Lesson 01: 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!
  
  Project Description - Autonomous Systems Interview Practice Project
  
  Project Rubric - Autonomous Systems Interview Practice Project