Abstract
This chapter presents a full tutorial on how to get started on performing parallel processing with ROS. The chapter starts with a guide on how to install the complete version of ROS on the Nvidia development boards Tegra K1, Tegra X1 and Tegra X2. The tutorial includes a guide on how to update the development boards with the latest OS, and configuring CUDA, ROS and OpenCV4Tegra so that they are ready to perform the sample packages included in this chapter. The chapter follows with a description on how to install CUDA in a computer with Ubuntu operating system. After that, the integration between ROS and CUDA is covered, with many examples on how to create packages and perform parallel processing over several of the most used ROS message types. The codes and examples presented on this chapter are available in GitHub and can be found under the repository in https://github.com/air-lasca/ros-cuda.
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Acknowledgements
The projects of this chapter were partially funded by National Counsel of Technological and Scientific Development of Brazil (CNPq), by Coordination for the Improvement of Higher Level People (CAPES) and by National Agency of Petroleum, Natural Gas and Biofuels (ANP) together with the Financier of Studies and Projects (FINEP) and Brazilian Ministry of Science and Technology (MCT) through the ANP Human Resources Program for the Petroleum and Gas Sector - PRH-ANP/MCT PRH10-UTFPR. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tegra X1 and Tegra K1 development boards used for this chapter.
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Dalmedico, N., Simões Teixeira, M.A., Barbosa, H.S., de Oliveira, A.S., Ramos de Arruda, L.V., Neves Jr, F. (2019). GPU and ROS the Use of General Parallel Processing Architecture for Robot Perception. In: Koubaa, A. (eds) Robot Operating System (ROS). Studies in Computational Intelligence, vol 778. Springer, Cham. https://doi.org/10.1007/978-3-319-91590-6_12
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