OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing

Dongxu Lv, Peijun Wang, Wentao Li, Peng Chen

2016

Abstract

To meet the requirements of railway track point cloud processing, an OpenCL-accelerated Point Feature His-togram method is proposed using heterogeneous computing to improve the low computation efficiency. Ac-cording to the characteristics of parallel computing of OpenCL, the data structure for point cloud storage is re-configured. With the kernel performance analysis by CodeXL, the data reading is improved and the load of ALU is promoted. In the test, it obtains 1.5 to 40 times speedup ratio compared with the original functions at same precision of CPU algorithm, and achieves better real-time performance and good compatibility to PCL.

Paper Citation

in Harvard Style

Lv D., Wang P., Li W. and Chen P. (2016). OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing . In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-198-4, pages 433-438. DOI: 10.5220/0005960304330438

in Bibtex Style

@conference{icinco16,
author={Dongxu Lv and Peijun Wang and Wentao Li and Peng Chen},
title={OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2016},
pages={433-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005960304330438},
isbn={978-989-758-198-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing
SN - 978-989-758-198-4
AU - Lv D.
AU - Wang P.
AU - Li W.
AU - Chen P.
PY - 2016
SP - 433
EP - 438
DO - 10.5220/0005960304330438