6533b7dafe1ef96bd126df3e

RESEARCH PRODUCT

Scalability of GPU-Processed 3D Distance Maps for Industrial Environments

Atle AalerudJoacim DybedalGeir Hovland

subject

0209 industrial biotechnologyComputer scienceNode (networking)Point cloud02 engineering and technologycomputer.software_genreFrame rateComputational science020901 industrial engineering & automationVoxelScalability0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingCollision detectionCentral processing unitcomputerComputingMethodologies_COMPUTERGRAPHICS

description

This paper contains a benchmark analysis of the open source library GPU-Voxels together with the Robot Operating System (ROS) in large-scale industrial robotics environment. Six sensor nodes with embedded computing generate real-time point cloud data as ROS topics. The overall data from all sensor nodes is processed by a combination of CPU and GPU on a central ROS node. Experimental results demonstrate that the system is able to handle frame rates of 10 and 20 Hz with voxel sizes of 4, 6, 8 and 12 cm without saturation of the CPU or the GPU used by the GPU-Voxels library. The results in this paper show that ROS, in combination with GPU-Voxels, can be used as a viable solution for real-time 3D collision detection and avoidance applications in relatively large-scale industrial environments.

yearjournalcountryeditionlanguage
2018-07-012018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)
https://doi.org/10.1109/mesa.2018.8449160