Method for clustering and identification of objects in laser scanning point clouds using dynamic logic

Abstract

Today there is a gap between a presence of various new equipment on the market which provides streams of various digital data about the environment, in particular in the form of laser scanning point clouds, and the lack of adequate efficient methods and software for information extraction from such data. A solution to the problem of bridging this gap is proposed on the basis of neural modeling field theory and dynamic logic (DL). We present a DL-based method of extracting and analyzing information from hybrid point clouds, which include not only spatial coordinates and intensity, but also the color of each point, and can be from multiple sources including terrestrial, mobile and airborne laser scanning data. The proposed method is significant for creating a fundamental theoretical basis for new application algorithms and software for many new applications, including building information modeling, “smart city” environment, etc. The proposed method is fairly new to solving various problems related to extracting semantically rich information from a nontraditional type of digital data, especially hybrid point clouds created from laser scanning. This method will allow to significantly expand the existing boundaries of knowledge in the field of extraction and analysis of information from various digital data, because neural modeling field theory and DL can improve the performance of relevant calculations and close the existing gap in analysis of digital images.