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short_course:lidar

# Short Course: LIDAR

Author: Blake Hament Email: blakehament@gmail.com
Keywords: tutorial, rangefinder, webcam, laser, LIDAR

This page outlines the content for a short course on sensors that use light to approximate distance, commonly known as Light Detection and Ranging or LIDAR.

image from http://www.alfoldron.hu/

image from NASA

## Motivation and Audience

This course is designed for engineering students with an interest in robotics. Recent developments in autonomous driving technologies have driven down the cost and size of LIDAR sensors, even while their performance has drastically improved. LIDAR data can be used for navigation and object identification in a wide range of autonomous applications. The course will cover everything from the theory of operation behind LIDAR to reading data from a LIDAR sensor.

I assume the reader has the following background and interests:

• Basic proficiency in CPP, Python, or other programming language
• Trigonometry proficiency
• Interest in engineering and autonomous systems

The rest of the course is organized as follows:

1. Week 1: 1D Rangefinding
2. Week 2: 2D Rangefinding
3. Week 3: 3D Rangefinding
4. Week 4: Visualizing Data in rviz (ROS)
5. Final

## Week 1: 1D Rangefinding

Theory of Operation: DIY Laser/Webcam Rangefinder
Trig Practice Problems

Theory of Operation: Time-of-Flight Laser Rangefinder
HW: Light Practice Problems

## Week 2: 2D Rangefinding

Theory of Operation: 2D LIDAR

Intro to Hokuyo/LMS SICK

LIDAR Data Structures

HW: Interpret instances of digital output from LIDAR sensor

Point Clouds, Reference Points, Control Points, Mesh

HW: Import a point cloud, Mesh, Export as .STL

## Week 3: 3D Rangefinding

Theory of Operation: 3D LIDAR

Frames of Reference, Quaternions

HW: Rotation practice problems

## Week 4: Visualizing Data in rviz (ROS)

Basic ROS Commands

Nodes, Topics, Publishers, Subscribers

HW: Write out step-by-step instructions for reading data from a Hokuyo and visualizing in rviz

## Final

The final exam consists of multiple choice questions on the above topics and a practical portion in which students demonstrate how to connect to and visualize data from a LIDAR sensor.