Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration
Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration
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DOI：
10.1109/lgrs.2012.2205130
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发表时间：
2013-03
影响因子：
4.8
通讯作者：
Xiangyun Hu;Xiaokai Li;Yongjun Zhang
中科院分区：
文献类型：
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作者：
Xiangyun Hu;Xiaokai Li;Yongjun Zhang
光探测和测距（LiDAR）系统的海量点云数据的快速滤波对于许多应用都很重要，例如城市地区数字高程模型的自动提取。我们提出了一个简单的扫描线为基础的算法，首先检测到当地的最低点，并把它们作为种子生长成地面段，通过使用坡度和高程。由于扫描线分割算法对每一行的处理都是独立的，因此可以通过并行计算自然地加速扫描线分割算法。此外，现代图形处理单元（GPU）可以用于显著加速并行处理。我们使用一条多达4800万个点的LiDAR点云，在错误率和时间性能方面测试了该算法。实验表明，该方法在GPU加速下，处理时间小于0.6s，可以得到满意的结果。
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.