ROS2 Point Clouds For Autonomous Self Driving Car using PCL

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 detecting  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.

Sections  :

1. Basic Data Understanding in CPP

2. Point cloud Algorithms and Segmentation

3. 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