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IN-HAND OBJECT LEARNING AND RECOGNITION USING 2D AND 3D INFORMATION

Bachelor's Thesis

Author: Irene Sanz Nieto

Branch: hydro-devel

Index

1.Introduction

2.Compile and run

2.1. Dependencies

2.2. Compiling

2.3 Running

  1. Doxygen documentation

  2. More info

  3. Introduction


This repository contains the software developed for a Bachelor's Thesis. It is an in-hand object training and recognition using 3D and 2D features.

The hardware used is a RGB-D camera, in this case a Kinect360 and a laptop running ubuntu 13.04.

This project uses the Groovy distro of the Robotic Operating System (ROS).

  1. Compile and run

2.1. Dependencies The aditional ROS packages needed are the following: openni_launch openni_tracker

Another ROS package also used is the pi_tracker (http://wiki.ros.org/pi_tracker), but it is compiled within the project since the code is slightly modified for compatibility.

2.2 Compiling

2.2.1. Using the terminal

Open a terminal (Ctrl + Alt + t) Enter the command : rosmake ocular

This command will build the whole project The compilation is done using Rosbuild.

2.2.1. Using QtCreator

To open the software as a QtCreator project, the only thing needed is to open the main CMakeLists.txt (sandbox/ocular/CMakeLists.txt) with QtCreator. This will parse the whole project. Afterwards, press the "build" icon to build the project.

2.3 Running

The code may be runned more easily through a launch file. In order to do so, enter the following command in a terminal:

roslaunch ocular ocular.launch

This launch file will open all the executables and nodes and nodelets needed for the project.

  1. Doxygen documentation

/todo 4. More info

/todo

About

This site will store the development of my Bachelor's Thesis regarding 3D Object Recognition

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