Parallel Algorithm of Generating DEM from LiDAR Point Clouds Based on Dynamic Load Balancing Strategy

  • Jiangsu Provincial Key Laboratory of Geographic Information Sciences and Technology, Nanjing 210023, China

Received date: 2014-12-10

  Revised date: 2015-01-09

  Online published: 2015-05-10


With the development of high performance computing, parallel processing has been widely used in analyzing LiDAR point clouds. Aiming at the loading unbalancing problem that exists in current parallel algorithms for generating DEM from LiDAR point clouds, this research implements a parallel algorithm which uses dynamic load balancing strategy to generate DEM from massive LiDAR points. The parallel algorithm is based on the master- slave scheduling strategy. The master processor adaptively partitions LiDAR data and generates several strips afterwards. The data strip may be horizontal or vertical based on the characteristic of LiDAR data. The slave processors generate raster DEM from discrete LiDAR points using spatial interpolation. Furthermore, we propose a dynamic scheduling strategy based on the quantity of tasks. The quantity of each task is measured by the number of points in data strip. Firstly, all processors count the point number for all data strips and the master processor creates a task queue. The task queue is arranged according to the point number of those data strips in a descending order from the largest to the smallest. Secondly, the master processor communicates with the slave processors to distribute these tasks dynamically, thus to help the slave processors achieve the load balancing. In this way, all of the data strips are processed from the largest to the smallest based on the computational complexity. Finally, we test the proposed parallel algorithm in a cluster. The cluster is composed of 24 cores. The volume of the LiDAR point clouds for testing is 30 GB, which contains about 1.2 billion points. The resolution of the target DEM is 1 meter, and the biggest speedup ratio of the parallel algorithm is 15.16. At the same time, we compare the dynamic scheduling strategy proposed in this paper with the static scheduling strategy. The result shows that the dynamic scheduling strategy proposed in this research achieves a better load balancing among all processors. Therefore, we can come to a conclusion that the parallel algorithm proposed in this research can significantly improve the efficiency of generating DEM from massive LiDAR point clouds.

Cite this article

REN Yibin, CHEN Zhenjie, CHENG Liang, LI Manchun, PIAN Yuzhe . Parallel Algorithm of Generating DEM from LiDAR Point Clouds Based on Dynamic Load Balancing Strategy[J]. Journal of Geo-information Science, 2015 , 17(5) : 531 -537 . DOI: 10.3724/SP.J.1047.2015.00531


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