Range Image Techniques for Fast Detection and Quantification of Changes in Repeatedly Scanned Buildings

A u g u s t 2 0 1 3 1 evolution that require a more subtle analysis of the measured scene. Several deformation analysis approaches for designated objects have been discussed in recent years. An approach that employs terrestrial laser scanning data of a lock gate was presented by Schäfer et al. (2004). This method enabled deformation detection through the construction of a uniform and regular grid from each scattered point cloud. Instead of simple thresholding of differences, Lindenbergh and Pfeifer (2005) proposed a method to detect deformation below the nominal single point measurement accuracy using statistical deformation analysis based on the comparison of laser points and fitted planar segments between different epochs. Similarly, Kenner et al. (2011) presented a detailed spatial (by superficially extending the movement) and temporal (the number of epochs) analysis to detect changes smaller than the single point accuracy, which was applied in the monitoring of erosive processes in a rock wall. Gosliga et al. (2006) implemented deformation analysis on a bored tunnel by means of terrestrial laser scanning that included an analysis of both deformation between different epochs and deformations of the fitted tunnel with respect to the design model. GirardeauMontaut et al. (2005) carried out a comparison study by using the Hausdorff distance as a measure for changes. They noted three issues to be addressed: point sampling variations between scans, computational costs of the Hausdorff distance, and real change discrimination. Zeibak and Filin (2007) also analyzed the potential artifacts that may affect the detection, which include resolution and object pose variations, occlusion and scanner-related artifacts (e.g., regions of no reflectance, range limitations, and noise). They proposed an approach for change detection through the use of TLS data in which the comparison is conducted through a mere image subtraction between the range images of a reference scan and the analyzed scan transformed into the reference frame to eliminate the artifacts mentioned above. As this method assumes that the transformation parameters are known, the reliability of image subtraction depends greatly on the registration accuracy between reference and analyzed scans. In general, the challenges of change detection of buildings sampled by terrestrial laser scans comprise the irregular point distribution, the varying scale and resolution within the scene (depending on depth), the large data volume in each scan and falsely detected changes caused by factors such as the occlusion of objects or object parts, “no-reflectance” regions, range limits, noise, and errors in the registration Abstract This paper proposes a new method for the detection and quantification of changes in buildings using terrestrial laser scanning data from different epochs. A refined registration process is implemented that utilizes an optimized version of the Iterative Closest Point (ICP) algorithm, which implements the search of adjacent points in terms of their scanning angles. For detecting changes, a novel 2d angular difference histogram is proposed to first determine point segments representing building parts from the raw scattered scans. Afterwards, Hausdorff distance-based change detection is innovatively integrated into the optimized ICP process to improve the efficiency of the entire algorithm. The detected changes are quantified in the final step by determining the total planar surface area of the changed facade. This approach is tested and illustrated on two real datasets. The change quantification results show that the accuracy of the changed area quantification is in the order of square centimeters.