Heuristic Filtering and 3d Feature Extraction from Lidar Data

The need for a fast, efficient and low cost algorithm for extracting 3D features in urban areas is increasing. Consequently, research in feature extraction has intensified. In this paper we present a new technique to reconstruct buildings and other 3D features in urban areas using LIDAR data only. We have tried to show that dense LIDAR (Light detection and ranging) data is very suitable for 3D reconstruction of urban features such as buildings. This concept is based on local statistical interpretations of fitting surfaces over small windows of LIDAR derived points. The consistency of the data with surfaces determines how they will be modeled. Initially, the data has been filtered to remove extraneous objects such as trees and undesired small features. Then, boundaries will be extracted for each facet using statistical information from the surface fitting procedure, and using inferences about the dominant direction. Building features extracted from actual dense LIDAR collected over the Purdue campus are presented in the paper.