Mohannad Al-Durgham

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.

On the Accuracy of LiDAR Derived Digital Surface Models

The use of LiDAR data for developing high resolution topographic models of urban and rural areas has been widespread for more than a decade. In particular, the derived high resolution digital surface models (DSMs) are commonly used as input (among other models and parameters) for predicting the coverage of flooded regions. The accuracy of such digital surface models varies depending on a number of factors, from system components (including positioning technology, e.g., GPS/INS, laser scanner and boresighting parameters), data processing (point clouds, interpolation) and signal-target/surface interaction (i.e., backscatter signal strength, laser incidence angle/geometry). The main objective of this paper is to quantify the absolute and relative accuracy of the derived digital surface models. LiDAR data collected over an urban area in Canada from an airborne platform is used for the analysis and compared to precise DGPS survey data. In addition to inter-comparative first order statistical measures, the iterative closest point matching of point clouds method is employed for a consistent analysis. Results provide an indication of the contributing factors to the total error budget for vertical heights in this region which is found to be at the 3 cm level (1 sigma).

A procedure for the registration and segmentation of heterogeneous lidar data

Laser scanning, whether airborne or terrestrial is being used nowadays for wide spectrum of applications. In addition, many advances have been introduced to the laser scanning technology in the last decade; thus resulting into increased performance in terms of the point density, scanner range, and expected point accuracy. On the other hand, users are encountering scenarios where the integration of various laser datasets becomes essential in order to avoid data gaps (e.g., missing building roofs in the terrestrial scans, or missing structure facades in the airborne case). This problem is usually solved seamlessly through a classical transformation when the average point accuracy is relatively homogeneous over a given dataset. However, this is not usually the case; in this work, we propose a workflow for the optimal registration of multisource point clouds using weighted conformal transformation. First, the individual scans are filtered and the local point attributes are populated through a data characterization step. Then, an ICPP-based weighted registration algorithm is performed over the entire datasets until convergence. Finally, our heterogeneous segmentation procedure is performed in a simultaneous fashion to ensure exploiting the full potential of a dataset. The performance of this algorithm in terms of correctness, automation level, and other factors is evaluated using real datasets with significant variations in point densities and accuracy.

Calibration and stability analysis of medium-format digital cameras

Recent developments in digital cameras in terms of an increase in size of the charged coupled device and the complementary metal oxide semiconductor arrays, as well as a reduction in costs, are leading to their use for traditional and new photogrammetric, surveying, and mapping functions. Such usage should be preceded by careful calibration of the implemented cameras in order to determine their interior orientation parameters. In addition, the wide diversity of expected users mandates the development of a convenient calibration procedure that does not require professional photogrammetrists and/or surveyors. This paper introduces a methodology for calibrating medium-format digital cameras using a test field consisting of straight lines and a few signalized point targets. A framework for the automatic extraction of the linear features and the point targets from the images, and for their incorporation into the calibration procedure, is presented and tested. In addition, the research introduces an approach for testing the camera stability, in which the degree of similarity between the bundles reconstructed from two sets of interior orientation parameters is quantitatively evaluated. Experimental results with real data proved the feasibility of the line-based self-calibration approach. In addition, the analysis of the internal characteristics of the utilized camera estimated from various calibration sessions revealed the cameras stability over a long period.