ROS2 Point Clouds For Autonomous Self Driving Car using PCL

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Muhammad Luqman

1:09:42

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1 - Basic Point Cloud Data Understanding

1 - ROS2 First Node.mp4

05:52

2 - PCL Node.mp4

06:45

3 - Creating Point Cloud.mp4

06:09

4 - RTAB Mapping with Depth Cameras.mp4

06:14

5 - Turtlebot3 mapping.mp4

07:01

6 - Driving Robot for Mapping.mp4

02:50

2 - Point cloud Algorithms and Segmentation

7 - Cloud Filtering.mp4

08:06

8 - Segmentation.mp4

06:50

9 - Clustering.mp4

11:21

10 - Path Creation.mp4

08:34

Description

3D Lidar Kitti Dataset and Depth Camera Custom Point Clouds

What You'll Learn?

Point Cloud Voxelizing and Clustering using PCL in CPP
Extractiong of Planner and Cylinderical Point Clouds
Object Extraction from 3D lidar Scan from Kitti Dataset
Creating Custom Point Cloud Maps using ROS2 and RTAB MAP

Who is this for?

Self Driving Car Egnineers

Software Developers

Perception Engineers

More details

Description
Welcome to our course on Point Clouds! In this course, we will explore the exciting world of 3D mapping and object detection using point clouds.
We will start with RTAB mapping, a powerful technique for creating accurate 3D maps using RGB-D cameras. Through hands-on projects, you will learn how to use this technique to generate high-quality point clouds from your own data.
Next, we will dive into the Kitti Dataset and explore how to use 3D lidars for object detection. We will teach you how to use advanced techniques for detectingÂ objects in real-time, such as lidar-based segmentation and clustering.
We will also cover ROS2, an essential tool for visualizing and processing point cloud data. With ROS2, you will learn how to use rviz and PCL to create stunning visualizations and analyze your point cloud data with ease.
In addition, we will explore cylindrical and planar segmentation, two important techniques for extracting meaningful information from your point cloud data. Through a series of hands-on exercises, you will learn how to use these techniques to accurately identify and classify objects in your point clouds.

SectionsÂ :
Basic Data Understanding in CPP
Point cloud Algorithms and Segmentation
Real World 3D Lidar Processing ( Up - coming )
Outcomes After this Course : You can create
1. Understanding of basic data structures and algorithms in CPP programming language, which is essential for implementing computer vision and machine learning applications.
2. Proficiency in implementing point cloud algorithms and segmentation techniques that are commonly used in computer vision applications such as object recognition, scene reconstruction, and robotics.
3. Ability to process real-world 3D Lidar data, which is essential for autonomous vehicle applications and other robotics applications that involve sensing and perception.

Software Requirements
UBUNTU 22.04 LTS
ROS2 Humble
Basics of C++

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Before buying take a look into this course GitHub repository
Who this course is for:
Self Driving Car Egnineers
Software Developers
Perception Engineers

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Automotive Engineering

Muhammad Luqman

Instructor's Courses

Luqman a Robotics Enthusiast ready to teach things that are hard to learn begin with.I am trying to make it easy to learn -> a simulation software ROS , which is becoming world's top leading robotics simulation frameworkIf you feel that you learned something through my courses , I will be happy :)COMSATS <3

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