Robot@Home, released by the MAPIR-UMA group in 2017, is a vast repository of both raw and processed data collected by a mobile robot in a number of apartments. The raw data encompasses +87K timestamped observations divided into RGB-D images (including both intensity and depth information) and 2D laser scans covering 40 scenarios.
Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. Robot@Home2 consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.
If you use Robot@Home2, please cite the following paper:
Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101490.
@article{gambrosio2023softwarex,
title = {The Robot@Home2 dataset: A new release with improved usability tools},
author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez},
journal = {SoftwareX},
volume = {23},
pages = {101490},
year = {2023},
month = {{jul}},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2023.101490},
url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863},
keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab}
}
This release is made up of a relational database file in SQLite format with all the original data and a size of only 2,2 GB. The image and scene files have been reorganized and now takes only 25,9 GB.
The database, named rh.db
, is a relational sql database accessible with the
SQLite engine that usually accompanies the python environment and is popularly
used in the development of current applications in both fixed (linux and
windows) and mobile environments. (android).
The data files have been organized into two main groups. On the one hand, the files with RGBD data (RGB images and depth images) and on the other the 3D scenes in point cloud files.
The intensity (RGB) and depth (D) image files have a standard png format so they can be opened directly. In addition, the files are linked to the data in the database through tables that relate them. Moreover, the database contains tables that relate the files of the new version with those of the old version.
In the case of 3D scene files, these are plain text files that store the coordinates and colors of the points that make up the 3D cloud.
You no longer need to waste time diving the obscure data formats (despite an API dataset.py for that is provided). Instead, you can simply surf the dataset through SQL queries or the new Python package.
Launched in 1991, Python has achieved enormous popularity in the scientific community in recent years. Python is an interpreted high-level general-purpose programming language with a many useful features. It's platform independent, simple, consistent and with a great code readability. Moreover, it has an extensive set of libraries that help to reduce development time.
Artificial Intelligence (AI) and Machine Learning (ML) projects differ from software projects in other areas due to differences in the technology stack and the skills needed to deal with them.
Python offers AI and ML programmers many features that help to develop and test complex algorithms. Even in Computer Vision (CV), there are solid software libraries that allow developers to focus on their research areas.
There are several different Python distributions, each one created with a different approach and for different audiences.
Robot@Home2 Toolbox is written in Python and works well with Anaconda which is a distribution of the Python and R programming languages for scientific computing. Of course, other distributions can be used to run the toolbox.
To install Anaconda in Linux you must follow these steps.
Download the Anaconda installer
$ cd ~/Downloads
$ wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh
replace
~/Downloads
with the path of your choice
Install the distribution
$ bash ~/Downloads/Anaconda3-2022.10-Linux-x86_64.sh
include the
bash
command regardless of whether or not you are using Bash shell.
Review and agree the license agreement. Accept the default install location.
When the installer prompts “Do you wish the installer to initialize Anaconda3 by running conda init?”, we recommend “yes”.
Finally, for the installation to take effect
$ source ~/.bashrc
For more detailed/updated installation information, go to Anaconda installation page.
Due to the graphic abundance of the installation procedure, we refer you to the specific Anaconda documentation page for installation on Windows.
Enter the command python
. This command runs the Python shell. If Anaconda is
installed and working, the version information it displays when it starts up
will include “Anaconda”
. To exit the Python shell, enter the quit()
command.
$ python
Python 3.9.16 (main, Jan 11 2023, 16:05:54)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> quit()
You can also display a list of installed packages and their versions running conda list
$ conda list
# packages in environment at /home/user/anaconda3:
#
# Name Version Build Channel
...
Click Start, search, or select Anaconda Prompt from the menu. After opening
Anaconda Prompt on the terminal enter the command python
. This command runs
the Python shell. If Anaconda is installed and working, the version
information it displays when it starts up will include Anaconda
. To exit
the Python shell, enter the command quit()
.
As in Linux you can also display a list of installed packages and their
versions running conda list
A virtual environment is a Python environment such that the Python interpreter, libraries and scripts installed into it are isolated from those installed in other virtual environments
When a virtual environment is active, the installations tools install Python packages into the virtual environment without needing to be told to do so explicitly and without interfering in other virtual environments.
That's the reason why it's recommended to work with a virtual environment specifically for Robot@Home2. To do that with conda
$ conda create --name rh python=3.9
change
rh
to a name of your choice
Robot@Home2 runs with python 3.6 or higher. Also, version 3.6 is recommended for Windows
once it has been created, it can already be activated
$ conda activate rh
to deactivate run
$ conda deactivate
Literate programming is a programming paradigm introduced by Donald Knuth in which a computer program is given an explanation of its logic in a natural language, such as English, interspersed with snippets of macros and traditional source code. The approach is typically used in scientific computing and in data science routinely for reproducible research and open access purposes.
On the other hand, the Jupyter *Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Additionally JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data.
Jupyter is an application of literate programming and Robot@Home2 includes Jupyter notebooks for introductions, easy learning, and technical explanations.
Installing Jupyter in Anaconda distribution is an easy task
$ conda install -c conda-forge jupyterlab
remember to previously activate your virtual environment with `conda activate` command
If you have followed the previous sections you have the right working environment to open this notebook with Jupyter to download and install both the toolbox and the dataset.
However, if jupyter notebook is not your choice right now you can try the following instructions.
Robot@Home2 Toolbox can be installed through the Python package manager.
-
Confirm you are in the right virtual environment
$ conda activate rh
-
Enter this command to install
robotathome
with Jupyter to run notebooks.$ pip install robotathome
pip
is a common Python package manager that is included in Anaconda and many other distributionsIf you have not previously installed
jupyterlab
you can do it right now adding theinteractive
option to thepip
command as follows:$ pip install robotathome[interactive]
interactive
will include jupyter and needed libraries. -
Run
python
and import therobotathome
library$ python Python 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import robotathome as rh >>> print (rh.__version__) 1.1.1
-
Congratulations ! the
robotathome
package has been installed successfully.
Robot@Home resides in Zenodo site where all data versions can be downloaded.
Latest version (v2.0.3) is composed of two
files: Robot@Home2_db.tgz
and Robot@Home2_files.tgz
. The first one contains the
database, and the second one contains the bunch of RGBD images and 3D scenes
You can choose to download it on your own or through the new brand toolbox.
In case you are considering Linux
$ wget https://zenodo.org/record/7811795/files/Robot@Home2_db.tgz
$ wget https://zenodo.org/record/7811795/files/Robot@Home2_files.tgz
check the files integrity
$ md5sum Robot@Home2_db.tgz
d34fb44c01f31c87be8ab14e5ecd0767 Robot@Home2_db.tgz
$ md5sum Robot@Home2_files.tgz
c55465536738ec3470c75e1671bab5f2 Robot@Home2_files.tgz
and to finish unzip files
$ pv /home/user/Downloads/Robot@Home2_db.tgz | tar -xzf - -C /home/user/WORKSPACE/
$ pv /home/user/Downloads/Robot@Home2_files.tgz | tar -xzf - -C /home/user/WORKSPACE/files
or even better, now you can do the same programmatically using the toolbox
import robotathome as rh
# Download files
rh.download('https://zenodo.org/record/7811795/files/Robot@Home2_db.tgz', '~/Downloads')
rh.download('https://zenodo.org/record/7811795/files/Robot@Home2_files.tgz', '~/Downloads')
# Compute md5 checksums
md5_checksum_db = rh.get_md5('~/Downloads/Robot@Home2_db.tgz')
md5_checksum_files = rh.get_md5('~/Downloads/Robot@Home2_files.tgz')
# Check the files integrity and download
if md5_checksum_db == 'd34fb44c01f31c87be8ab14e5ecd0767':
rh.uncompress('~/Downloads/Robot@Home2_db.tgz', '~/WORKSPACE')
else:
print('Integrity of Robot@Home2_db.tgz is compromised, please download again')
if md5_checksum_files == 'c55465536738ec3470c75e1671bab5f2':
rh.uncompress('~/Downloads/Robot@Home2_files.tgz', '~/WORKSPACE/files')
else:
print('Integrity of Robot@Home2_files.tgz is compromised, please download again')