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You're reading from ROS Programming: Building Powerful Robots Design, build and simulate complex robots using the Robot Operating System

Product type Course

Published in Mar 2018

Publisher Packt

ISBN-13 9781788627436

Length

Edition 1st Edition

Languages

MATLAB

Tools

ROS

Concepts

Design

Authors (5):

Anil Mahtani

Aaron Martinez

Lentin Joseph

Enrique Fernandez Perdomo

Luis S√°nchez

+1 more

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Table of Contents (37) Chapters

Title page

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Preface

1. Getting Started with ROS

2. ROS Architecture and Concepts FREE CHAPTER

3. Visualization and Debugging Tools

4. The Navigation Stack - Robot Setups

5. The Navigation Stack - Beyond Setups

6. Manipulation with MoveIt!

7. Using Sensors and Actuators with ROS

8. Computer Vision

9. Point Clouds

10. Working with 3D Robot Modeling in ROS

11. Simulating Robots Using ROS and Gazebo

12. Working with Pluginlib, Nodelets, and Gazebo Plugins

13. Writing ROS Controllers and Visualization Plugins

14. Interfacing I/O Boards, Sensors, and Actuators to ROS

15. Programming Vision Sensors using ROS, Open-CV, and PCL

16. Building and Interfacing Differential Drive Mobile Robot Hardware in ROS

17. Exploring the Advanced Capabilities of ROS-MoveIt!

18. ROS for Industrial Robots

19. Troubleshooting and Best Practices in ROS

20. Face Detection and Tracking Using ROS, OpenCV and Dynamixel Servos

21. Building a Siri-Like Chatbot in ROS

22. Controlling Embedded Boards Using ROS

23. Teleoperate a Robot Using Hand Gestures

24. Object Detection and Recognition

25. Deep Learning Using ROS and TensorFlow

26. ROS on MATLAB and Android

27. Building an Autonomous Mobile Robot

28. Creating a Self-Driving Car Using ROS

29. Teleoperating a Robot Using a VR Headset and Leap Motion

30. Controlling Your Robots over the Web

1. Bibliography

2. Other Books You May Enjoy

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Index

Detecting and recognizing objects from 3D meshes

After installing these packages, let's start the detection. What are the procedures involved? Here are the main steps:

Building a CAD model of the object or capturing its 3D model
Training the model
Detecting the object using the trained model

The first step in the recognition process is building the 3D model of the desired object. We can do it using a CAD tool, or we can capture the real object using depth-sensing cameras. If the object is rigid, then the best procedure is CAD modelling, because it will have all the 3D information regarding the object. When we try to capture and build a 3D model, it may have errors and the mesh may not be look like the actual object because of the accumulation of errors in each stage. After building the object model, it will be uploaded to the object database. The next phase is the training of the uploaded object on the database. After training, we can start the detection process. The detection process will start...

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Authors (5)

Lentin Joseph

Lentin Joseph is an author and robotics entrepreneur from India. He runs a robotics software company called Qbotics Labs in India. He has 7 years of experience in the robotics domain primarily in ROS, OpenCV, and PCL. He has authored four books in ROS, namely, Learning Robotics using Python, Mastering ROS for Robotics Programming, ROS Robotics Projects, and Robot Operating System for Absolute Beginners. He is currently pursuing his master's in Robotics from India and is also doing research at Robotics Institute, CMU, USA.

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Luis Sánchez

Luis Snchez has completed his dual master's degree in electronics and telecommunication engineering at the University of Las Palmas de Gran Canaria. He has collaborated with different research groups as the Institute for Technological Development and Innovation (IDETIC), the Oceanic Platform of Canary Islands (PLOCAN), and the Institute of Applied Microelectronics (IUMA) where he actually researches on imaging super-resolution algorithms. His professional interests lie in Computer Vision, signal processing, and electronic design applied on robotics systems. For this reason, he joined the AVORA team, a group of young engineers and students working on the development of Underwater Autonomous Vehicles (AUV) from scratch. Inside this project, Luis has started developing acoustic and Computer Vision systems, extracting information from different sensors such as hydrophones, sonar, or camera. With a strong background gained in marine technology, Luis cofounded Subsea Mechatronics, a young start-up, where he works on developing remotely operated and autonomous vehicles for underwater environments. Here's what Dario Sosa Cabrera, a marine technologies engineer and entrepreneur (and the cofounder and maker of LPA Fabrika: Gran Canaria Maker Space) has to say about Luis: "He is very enthusiastic and an engineer in multiple disciplines. He is responsible for his work. He can manage himself and can take up responsibilities as a team leader, as demonstrated at the euRathlon competition. His background in electronics and telecommunications allows him to cover a wide range of expertise from signal processing and software, to electronic design and fabrication." Luis has participated as a technical reviewer of the previous version of Learning ROS for Robotics Programming and as a cowriter of the second edition.

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Enrique Fernandez Perdomo

Enrique Fernandez Perdomo has a PhD in Computer Engineer from the ULPGC. In his PhD thesis, he investigated path planning algorithms for Autonomous Underwater Gliders, and did an internship at the CIRS/VICOROB research group at the University of Girona. He also worked with ground robots and is currently a senior Robotics Engineer in the Autonomy Team at Clearpath Robotics since 2015. He was a member of the navigation department at PAL Robotics since 2013. He has good programming skills in C++ and Python, and develops wheeled controllers, a tele-operation infrastructure for both the wheeled and biped robots (REEM and REEM-C), SLAM algorithms, laser and visual localization, and autonomous navigation based on the ROS navigation stack. Finally, he worked on the project that develop a low-cost mobile base for high payload and retail applications, which is used on the TiaGo robot. Now at Clearpath Robotics, he works on SLAM and localization algorithms and has participated in the OTTO project.

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Aaron Martinez

Aaron Martinez is a computer engineer, entrepreneur, and expert in digital fabrication. He did his master's thesis in 2010 at the IUCTC (Instituto Universitario de Ciencias y Tecnologias Ciberneticas) in the University of Las Palmas de Gran Canaria. He prepared his master's thesis in the field of telepresence using immersive devices and robotic platforms. After completing his academic career, he attended an internship program at The Institute for Robotics in the Johannes Kepler University in Linz, Austria. During his internship program, he worked as part of a development team of a mobile platform using ROS and the navigation stack. After that, he was involved in some projects related to robotics; one of them is the AVORA project in the University of Las Palmas de Gran Canaria. In this project, he worked on the creation of an AUV to participate in the Student Autonomous Underwater Challenge-Europe (SAUC-E) in Italy. In 2012, he was responsible for manufacturing this project; in 2013, he helped to adapt the navigation stack and other algorithms from ROS to the robotic platform. Recently, Aaron created his own company named SubSeaMechatronics, SL. This company works with projects related with underwater robotics and telecontrol systems. They are also designing and manufacturing subsea sensors. The company manufactures devices for other companies and research and development institutes. Aaron has experience in many fields, such as programming, robotics, mechatronics, and digital fabrication as well as many devices, such as Arduino, BeagleBone, Servers, and LIDAR, and nowadays he is designing in SubSeaMechatronics SL some robotics platforms for underwater and aerial environments.

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Anil Mahtani

Anil Mahtani is a computer scientist who has dedicated an important part of his career to underwater robotics. He first started working in the field with his master thesis, where he developed a software architecture for a low-cost ROV. During the development of his thesis, he also became the team leader and lead developer of AVORA, a team of university students that designed and developed an autonomous underwater vehicle for the Students Autonomous Underwater Challenge Europe (SAUC-E) in 2012. That same year, Anil Mahtani completed his thesis and his MSc in Computer Science at the University of Las Palmas de Gran Canaria and then became a Software Engineer at SeeByte Ltd, a world leader in smart software solutions for underwater systems. In 2015, he joined Dell Secureworks as a Software Engineer, where he applies his knowledge and skills toward developing intrusion detection and prevention systems. During his time at SeeByte Ltd, Anil Mahtani played a key role in the development of several semi-autonomous and autonomous underwater systems for the military and oil and gas industries. In those projects, he was heavily involved in the development of autonomy systems, the design of distributed software architectures, and low-level software development and also contributed in providing Computer Vision solutions for front-looking sonar imagery. At SeeByte Ltd, he also achieved the position of project manager, managing a team of engineers developing and maintaining the internal core C++ libraries. His professional interests lie mainly in software engineering, algorithms, data structures, distributed systems, networks, and operating systems. Anil's main role in robotics is to provide efficient and robust software solutions, addressing not only the current problems at hand but also foreseeing future problems or possible enhancements. Given his experience, he is also an asset when dealing with Computer Vision, machine learning, or control problems. Anil has also interests in DIY and electronics, and he has developed several Arduino libraries, which he has contributed back to the community.

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You're reading from ROS Programming: Building Powerful Robots Design, build and simulate complex robots using the Robot Operating System

Table of Contents (37) Chapters

Detecting and recognizing objects from 3D meshes

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Authors (5)

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