机载LiDAR点云滤波综述

Review on Airborne LiDAR Point Cloud Filtering

机载LiDAR点云滤波是点云数据处理中的关键步骤,大量国内外专家学者对点云滤波算法进行了针对性的研究。近年来滤波算法发展迅速,不断提出各种具有新的理论背景的滤波算法。因此,急需对现有的各种滤波算法进行更为系统的总结。在前人研究的基础上,将点云滤波算法归纳为六类,详细阐述各类滤波算法的原理、实现方法以及所存在的问题。采用国际摄影测量与遥感学会提供的标准数据对各类代表性算法的滤波精度进行了横向比较,总结各类算法的优缺点。最后,对如何进一步提高点云滤波算法的精度以及稳健性进行了展望。该综述有利于点云数据处理研究人员对滤波算法有更为系统、清晰、准确的认识,有望为进一步拓展点云滤波算法以及提高点云后处理精度做出贡献。

Airborne LiDAR point cloud filtering is a key step in point cloud processing. Lots of experts and scholars at home and abroad are doing research on point cloud filtering. In recent years, filtering is developed very fast and many other algorithms based on new theoretical background are proposed. Thus, it is urgent to summarize all kinds of filtering algorithms systematically. We classified all the algorithms into six categories based on the previous studies. The principles, implementation steps and existed problems of each class were also elaborated. This paper adopted the data sets provided by the International Society for Photogrammetry and Remote Sensing （ISPRS） to compare the accuracy of each representative algorithm in each class and summarized their advantages and disadvantages. Last but not least, we provided some advices on how to further improve the accuracy and robustness of filtering algorithms. The review will be beneficial to point cloud data processing researchers to have more systematic, clear and accurate knowledge on filtering algorithms. It is also expected that this paper would make some contributions on extending filtering algorithms and improving point cloud post processing precision.