A Framework for the Registration and Segmentation of Heterogeneous Lidar Data

This article describes how Light Detection and Ranging (LIDAR) has been established as a mainstream tool for the acquisition of three dimensional point data over the past few years. Besides the conventional mapping missions, LIDAR has also proved to be very effective for a wide range of applications such as forestry, urban planning, structural deformation analysis, and reverse engineering. In the context of a national dataset, it is safe to assume that multiple laser scanners are under different conditions in order to collect data. Current registration and segmentation algorithms assume homogeneity in the local point density and accuracy, which is an invalid assumption that cannot be tolerated. As a consequence of the wide range of LIDAR sensors that are currently available, it is becoming crucial to develop algorithms for the registration and segmentation of LIDAR data with significantly varying characteristics, for example, varying point density and accuracy. A methodology for the optimal registration and segmentation of heterogeneous LIDAR data is presented in this article. An example of integrating airborne and terrestrial laser scans is also presented, which is followed by a discussion of the pros and cons of the integration process.