Filtering method for 3D laser scanning point cloud

Abstract

In recent years, with the rapid development of the hardware and software of the three-dimensional model acquisition, three-dimensional laser scanning technology is utilized in various aspects, especially in space exploration. The point cloud filter is very important before using the data. In the paper, considering both the processing quality and computing speed, an improved mean-shift point cloud filter method is proposed. Firstly, by analyze the relevance of the normal vector between the upcoming processing point and the near points, the iterative neighborhood of the mean-shift is selected dynamically, then the high frequency noise is constrained. Secondly, considering the normal vector of the processing point, the normal vector is updated. Finally, updated position is calculated for each point, then each point is moved in the normal vector according to the updated position. The experimental results show that the large features are retained, at the same time, the small sharp features are also existed for different size and shape of objects, so the target feature information is protected precisely. The computational complexity of the proposed method is not high, it can bring high precision results with fast speed, so it is very suitable for space application. It can also be utilized in civil, such as large object measurement, industrial measurement, car navigation etc. In the future, filter with the help of point strength will be further exploited.