CK repository with medium data set (20 frames) for the CK-powered SLAMbench

Supporting data sets in the [Collective Knowledge format (CK)] to be easily plugged in to the CK-based research workflows for SLAMBench

Minimal CK installation

The minimal installation requires:

• Python 2.7 or 3.3+ (limitation is mainly due to unitests)
• Git command line client.

Linux/MacOS

You can install CK in your local user space as follows:

$ git clone http://github.com/ctuning/ck
$ export PATH=$PWD/ck/bin:$PATH
$ export PYTHONPATH=$PWD/ck:$PYTHONPATH

You can also install CK via PIP with sudo to avoid setting up environment variables yourself:

$ sudo pip install ck

Windows

First you need to download and install a few dependencies from the following sites:

• Git: https://git-for-windows.github.io
• Minimal Python: https://www.python.org/downloads/windows

You can then install CK as follows:

 $ pip install ck

or

 $ git clone https://github.com/ctuning/ck.git ck-master
 $ set PATH={CURRENT PATH}\ck-master\bin;%PATH%
 $ set PYTHONPATH={CURRENT PATH}\ck-master;%PYTHONPATH%

CK workflow installation for SLAMBench (PAMELA project)

$ ck pull repo:reproduce-pamela-project
$ ck pull repo:reproduce-pamela-project-medium-dataset

See ck-slambench for more info.

Support

The non-profit cTuning foundation (France) and dividiti Ltd (UK/US) help academic and industrial projects to use Collective Knowledge framework (CK) and implement sustainable and portable research software, share artifacts and workflows as reusable and customizable components, crowdsource and reproduce experiments, enable collaborative AI/SW/HW co-design from IoT to supercomputers to trade-off speed, accuracy, energy, size and costs, accelerate knowledge discovery, and facilitate technology transfer. Contact them for further details.