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所在平台: Udemy |
课程主页: https://www.udemy.com/course/robotics-with-ros-autonomous-drone-with-path-planning-slam/
课程评论：没有评论
课程名称：学习构建基于ROS2的无人机计算机视觉无人机 课程概述：本课程已更新至ROS2 Humble版本，评价基于旧版本的课程。新版本提供全新的项目和解释方式，定会让您耳目一新！本课程将深入探讨ROS2 Humble，包含全新项目和增强的讲解，承诺提升您的学习体验。课程重点在于将Hector无人机及其传感器与Python节点进行接口，您将学习编写算法以实现各种无人机行为的Gazebo仿真。在整个课程中，您将获得实用的无人机控制经验，使用ROS2 C++节点，结合OpenCV的Python实现高级计算机视觉算法。 课程亮点： - 更新至ROS2 Humble：享受ROS2 Humble环境中的最新改进与更新。 - 增强的学习体验：得益于新项目和精细化的教学方法。 - 互动仿真：通过在Gazebo中与Hector无人机互动，实现实践动手学习。 - 高级计算机视觉：利用OpenCV的Python实施计算机视觉算法。 项目内容： - 使用ROS2 C++节点进行无人机控制：通过创建和管理ROS2 C++节点，培养无人机控制的基本技能。 - 通过计算机视觉跟随地面车辆的无人机：实施计算机视觉技术，使无人机能够自主跟随地面车辆。 - 无人机执行动态目标导航行为：开发和完善动态导航算法，使无人机能高效地移动到指定目标。 课程流程： - Hector无人机和传感器介绍：学习如何将无人机的传感器与Python节点接口，为后续项目奠定基础。 - 使用OpenCV进行计算机视觉：应用OpenCV库函数完成高级计算机视觉任务，提升无人机的能力和性能。 软件要求：Ubuntu 22.04、ROS2 Humble
Course Updated to ROS2 Humble:Rating is for OLD version of this course. The New update to projects and way of explaining is what you are going to love:)This updated course offers a comprehensive dive into ROS2 Humble, featuring all-new projects and enhanced explanations that promise to elevate your learning experience. The course is centered around interfacing Hector Drone and its sensors with Python nodes, where you will learn to write algorithms for various drone behaviors within the Gazebo simulation. Throughout the course, you will gain practical experience in drone control using ROS2 C++ nodes, leveraging OpenCV for Python to execute advanced computer vision algorithms in your final project.Course Highlights:Updated to ROS2 Humble: Enjoy the latest improvements and updates in the ROS2 Humble environment.Enhanced Learning Experience: Benefit from new projects and a refined teaching approach.Interactive Simulations: Engage with Hector Drone in Gazebo for practical, hands-on learning.Advanced Computer Vision: Utilize OpenCV for Python to implement computer vision algorithms.Projects:Drone Controlling using ROS2 C++ Nodes: Develop essential skills for drone control by creating and managing ROS2 C++ nodes.Drone Following Ground Vehicles through Computer Vision: Implement computer vision techniques to enable a drone to follow ground vehicles autonomously.Drone Performing Dynamic Go-to-Goal Behavior: Develop and fine-tune algorithms for dynamic navigation, allowing the drone to move efficiently to a designated goal.Course Workflow:Introduction to Hector Drone and Sensors: Learn to interface the drone's sensors with Python nodes, setting the foundation for subsequent projects.Computer Vision with OpenCV: Apply OpenCV library functions for advanced computer vision tasks, enhancing your drone's capabilities and performance.Software Requirements Ubuntu 22.04ROS2 Humble
