激光与光电子学进展, 2018, 55 (4): 042801, 网络出版: 2018-09-11   

基于多光谱数据指导的偏度平衡点云滤波 下载: 838次

Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data
作者单位
中北大学信息与通信工程学院, 山西 太原 030051
摘要
针对现有激光雷达(LiDAR)点云滤波方法无法有效排除数字表面模型(DSM)中数据空洞干扰的问题,提出了基于多光谱数据指导的偏度平衡点云滤波方法。该方法将多光谱数据引入点云滤波并将其作为引导图像,实现了与噪声点光谱相似点的快速去噪。实验结果表明,该方法有效排除了数据空洞对点云滤波造成的干扰,所得到的滤波误差与原有偏度平衡点云滤波方法相比减少了0.4%~0.8%;与目前流行的基于支持向量机(SVM)的滤波算法相比,该方法的误差减少了0.1%~0.4%。
Abstract
Aim

ing at the problem that the existing light detection and ranging (LiDAR) point cloud filtering method cannot effectively exclude the data hole interference in the digital surface model (DSM), a skewness balance point cloud filtering method based on multispectral data guidance is proposed. This method introduces the multispectral data into the point cloud filter as the guiding image to realize the fast denoising with the spectral similarity of the noise points. The experimental results show that this method can effectively eliminate the interference caused by the data hole to the point cloud filtering, and the obtained filtering error is reduced by 0.4%-0.8% compared with the original skewness point cloud filtering method. Compared with the popular filter algorithm based on support vector machines (SVM), the error of this method is reduced by 0.1%-0.4%.

韩晓峰, 杨风暴, 卫红, 李大威, 刘丹. 基于多光谱数据指导的偏度平衡点云滤波[J]. 激光与光电子学进展, 2018, 55(4): 042801. Xiaofeng Han, Fengbao Yang, Hong Wei, Dawei Li, Dan Liu. Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data[J]. Laser & Optoelectronics Progress, 2018, 55(4): 042801.

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