Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration

• Published in: IEEE geoscience and remote sensing letters Vol. 10; no. 2; pp. 308 - 312
• Main Authors: Xiangyun Hu, Xiaokai Li, Yongjun Zhang
• Format: Journal Article
• Language: English
• Published: IEEE 01.03.2013
• Subjects: Acceleration; fast filtering; Graphics processing unit; graphics processing unit (GPU); Instruction sets; Laser radar; light detection and ranging (LiDAR); Parallel processing; Prediction algorithms; Remote sensing; scan line; segmentation; Urban areas
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2012.2205130

The fast filtering of massive point cloud data from light detection and ranging (LiDAR) systems is important for many applications, such as the automatic extraction of digital elevation models in urban areas. We propose a simple scan-line-based algorithm that detects local lowest points first and treats them as the seeds to grow into ground segments by using slope and elevation. The scan line segmentation algorithm can be naturally accelerated by parallel computing due to the independent processing of each line. Furthermore, modern graphics processing units (GPUs) can be used to speed up the parallel process significantly. Using a strip of a LiDAR point cloud, with up to 48 million points, we test the algorithm in terms of both error rate and time performance. The tests show that the method can produce satisfactory results in less than 0.6 s of processing time using the GPU acceleration.