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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ros2-point-clouds-for-autonomous-self-driving-car-using-pcl/
课程评论：没有评论
本课程是关于使用ROS2和PCL进行自动驾驶汽车点云处理的。 **课程内容概述：** * **RTAB 建图：** 学习如何使用RGB-D摄像头进行精确的3D地图绘制，并生成高质量的点云数据。 * **KITTI 数据集与3D LiDAR 物体检测：** 探索如何利用3D LiDAR进行实时物体检测，包括基于LiDAR的分割和聚类等高级技术。 * **ROS2 应用：** 掌握ROS2工具，用于点云数据的可视化和处理，包括使用rviz和PCL进行数据分析和创建可视化效果。 * **点云分割技术：** 学习圆柱和平面分割等重要技术，用于提取点云数据中的有意义信息，并实现物体的识别和分类。 **课程结构：** * C++基础数据理解 * 点云算法与分割 * 真实世界3D LiDAR处理 (即将推出) **学习成果：** * 掌握C++基础数据结构和算法，为实现计算机视觉和机器学习应用奠定基础。 * 熟练掌握点云算法和分割技术，应用于物体识别、场景重建和机器人技术。 * 能够处理真实世界的3D LiDAR数据，为自动驾驶汽车和其他机器人感知应用做好准备。 **软件要求：** * UBUNTU 22.04 LTS * ROS2 Humble * C++基础知识 **重要提示：** 在购买课程前，请查看课程的GitHub仓库了解更多详情。syllabus: None
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 :Basic Data Understanding in CPPPoint cloud Algorithms and SegmentationReal 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 LTSROS2 HumbleBasics of C++----------------------------------------- Before buying take a look into this course GitHub repository
