Geniux Rocko v21.06 New release of the Geniux Rocko manifest. It contains a list of Git repositories and their respective commit hashes that allow building images and SDKs defined by the meta-gnss-sdr OpenEmbedded layer Geniux Rocko v21.06 is a customized embedded Linux distribution based on Yocto Project version 2.4.4. The name Geniux comes from GNSS-SDR for Embedded GNU/Linux. More info at https://gnss-sdr.org/docs/tutorials/cross-compiling/ Changes with respect to Geniux Rocko 21.01 Updated meta-raspberrypi layer commit. Removed unused meta-ti and meta-odroid repos. Use linux-xlnx_adi-2019_R1 kernel for zedboard-zynq7 and zcu102-zynqmp machines, based on Linux kernel 4.14. Compatible with PetaLinux Tools v2018.3. Updated gnss-sdr recipe: Remove python-six dependency, add ENABLE_FPGA as building option by default, other syntax improvements, update to latest upstream next branch commit. Updated pugixml version to 1.11.4. Updated gflags version to 2.2.2. Updated glog version to 0.4.0. Updated matio version to 1.5.21. Updated Armadillo version to 10.5.0. Updated GoogleTest version 1.11.0. Improved packaging of gpstk. Improved gtkdatabox recipe. Cleaner image definitions. Transition of the https://github.com/carlesfernandez/yocto-geniux/ repo default branch name from "master" to "main". Do not inhibit package and package debug split. Updated README.md file. How to build images and the SDK With Docker already installed on your system, build the SDK and images for your preferred machine: $ git clone https://github.com/carlesfernandez/yocto-geniux $ cd yocto-geniux $ ./geniux-builder.sh rocko 21.06 zedboard-zynq7 or $ ./geniux-builder.sh rocko 21.06 zcu102-zynqmp The generated yocto-geniux images also provide an interactive mode that allows users to make changes, experiment, fine-tune, and generate their own custom images and SDKs according to their specific requirements in a virtualized environment. Please check the README.md file of that repository for instructions. Copyright and License Copyright: © 2016-2021 Carles Fernández-Prades, CTTC. All rights reserved. The content of this repository is released under the MIT license. Acknowledgements This work was partially supported by the Spanish Ministry of Science, Innovation, and Universities through the Statistical Learning and Inference for Large Dimensional Communication Systems (ARISTIDES, RTI2018-099722-B-I00) project.