Space-Filling Curve Based Point Clouds Index

Managing large volume points clouds data generated from laser scanner is a challenging problem in Geographic Information System (GIS) and spatial database. Based on analyzing the pros and cons of the existing management methods, this paper presents a method to manage lidar data in databases based on the Hilbert space-filling curve. Each lidar data point (X, Y, and Z) is encoded (indexed) by the 3-D Hilbert curve. Data points are organized together according to their Hilbert codes. The initial encoding level of Hilbert curve is determined by the total number of points and the target record size. The data points are first encoded with this initial level Hilbert curve. After refining and combining processes, the data volume of each group is controlled under the desired size. One record in database represents one data group; the binary blob of the record contains all the data points in one group. Details on constructing 3-D Hilbert curve are discussed. Typical query process “window query” is implemented. Reported in this paper are results based on synthetic and real lidar data collected from ground tripod lidar and airborne lidar equipments.