ROS_Kinetic_23 ROS流行版本和相关书籍汇总

本文介绍了ROS(机器人操作系统)的主流版本Hydro、Indigo、Jade和Kinetic,并重点推荐了Kinetic版本。此外,还列举了一系列中文和英文参考书籍,包括《ROS机器人程序设计》等,为ROS的学习提供了丰富的资料。

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目前,ROS使用的主流版本主要是下面四种:Hydro,Indigo,Jade,Kinetic

Distro

Release date

Poster

Tuturtle, turtle in tutorial

EOL date

ROS Kinetic Kame
(Recommended)

May 23rd, 2016

Kinetic Kame

Kinetic Kame

May, 2021

ROS Jade Turtle

May 23rd, 2015

Jade Turtle

Jade Turtle

May, 2017

ROS Indigo Igloo

July 22nd, 2014

I-turtle

I-turtle

April, 2019
(Trusty EOL)

ROS Hydro Medusa

September 4th, 2013

H-turtle

H-turtle

May, 2015

官方推荐版本为Kinetic。但是这四个版本的区别不算大,例如Hydro的源码无需

改动或很少改动就用于Kinetic,因此参考书只要是Hydro版本之后,都可以。

 

参考网址:1.ROS电子书及相关资料全集
                  2.http://books.exbot.net/

除了官网等资源,ROS相关参考书使我收获非常大,主要如下:

中文:

1 开源机器人操作系统-ROS


2 ROS机器人程序设计



3 ROS开源机器人控制基础



4 ROS入门实例



5 ExBotBooks


英文:

1 Learning Robotics Using Python



2 Learning ROS for Robotics Programming Second Edition



3 Mastering ROS for Robotics Programming



4 Programming Robots with ROS - A Practical introduction to the Robot Operating System



5 Robot Operating System (ROS) - The Complete Reference (Volume 1)



6 ROS By Example Volume 1 - A Do-It-Yourself Guide to the Robot Operating System



7 ROS By Example Volume 2 - Packages and Programs for Advanced Robot Behaviors


8 Learning ROS for Robotics Programming - Third Edition

 

9 ROS Robotics By Example


10 A Gentle Introduction to ROS

 

ROS和那些turtle


书中有诗和远方。


--


### ROS Kinetic Calibration Tutorial and Resources For users interested in calibrating sensors or other components within the ROS Kinetic environment, several tutorials and resources are available. The `pluginlib` package provides an abstract framework for creating and using plugins which might be useful when developing custom calibration nodes[^1]. However, specific to calibration tasks, one of the most comprehensive guides comes from the official ROS wiki documentation. The Camera Calibration tool is part of the image_pipeline suite and allows performing camera intrinsic parameter estimation through a graphical user interface. This process involves capturing images containing a known pattern such as a chessboard grid placed at different orientations relative to the camera lens. After collecting enough samples, running the calibration node will compute focal length, principal point position, distortion coefficients among others parameters necessary for undistorting captured frames during operation. Another important aspect covered by these materials includes extrinsic parameters determination between multiple cameras mounted on robots like TurtleBot where accurate alignment ensures better performance especially while navigating environments based upon visual SLAM algorithms. Instructions provided explain not only theoretical concepts behind each step involved but also practical considerations regarding setup preparation before starting actual measurements. Moreover, integrating self-created maps alongside simulation platforms enhances testing capabilities without requiring physical hardware availability all times thus facilitating development cycles significantly according to explanations found elsewhere concerning Stage integration techniques specifically tailored towards mobile bases equipped with laser scanners capable of generating occupancy grids used later inside Gazebo worlds after proper adjustments made via configuration files following guidelines outlined thereupon too[^3]. Lastly, expanding workspaces beyond base installations enables incorporating specialized libraries supporting advanced functionalities required throughout various stages ranging from data acquisition up until post-processing analysis phases thanks largely due contributions coming directly out-of-the-box once extra dependencies get added properly following instructions laid down hereunder about managing releases efficiently so projects benefit fully from community efforts put forth collectively over time ensuring compatibility across diverse setups encountered regularly nowadays within research labs around globe alike[^5]. ```bash roslaunch camera_calibration cameracalibrator.launch \ image:=<your_image_topic> \ camera_name:=<your_camera_name> \ calib_url:=<path_to_your_calib_file> ```
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