You're reading from Hands-On Vision and Behavior for Self-Driving Cars Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800203587

Length 374 pages

Edition 1st Edition

Languages

Python

Tools

OpenCV

Concepts

Computer Vision

Authors (2):

Krishtof Korda

Luca Venturi

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: OpenCV and Sensors and Signals

2. Chapter 1: OpenCV Basics and Camera Calibration FREE CHAPTER

3. Chapter 2: Understanding and Working with Signals

4. Chapter 3: Lane Detection

5. Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks

6. Chapter 4: Deep Learning with Neural Networks

7. Chapter 5: Deep Learning Workflow

8. Chapter 6: Improving Your Neural Network

9. Chapter 7: Detecting Pedestrians and Traffic Lights

10. Chapter 8: Behavioral Cloning

11. Chapter 9: Semantic Segmentation

12. Section 3: Mapping and Controls

13. Chapter 10: Steering, Throttle, and Brake Control

14. Chapter 11: Mapping Our Environments

15. Assessments

16. Other Books You May Enjoy

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SLAM with an Ouster lidar and Google Cartographer

This is the moment you have been waiting for: building maps with hands-on experience using Cartographer and an Ouster lidar sensor!

An Ouster lidar was chosen for this hands-on example because it has a built-in IMU, which is needed to perform SLAM. This means that you don't need to purchase another sensor to provide the inertial data.

The example you will see is the offline processing of data collected from an Ouster sensor and is adapted from the work of Wil Selby. Please visit Wil Selby's website home page for more cool projects and ideas: https://www.wilselby.com/.

Selby also has a related project that performs the SLAM online (in real time) for a DIY driverless car in ROS: https://github.com/wilselby/diy_driverless_car_ROS.