MANAGING UNCERTAINTY IN THE DETECTION OF CHANGES FROM TERRESTRIAL LASER SCANNERS DATA

We present in this paper an algorithm for the detection of changes based on terrestrial laser scanning data. Detection of changes has been a subject for research for many years, seeing applications such as motion tracking, inventory-like comparison and deformation analysis as only a few examples. One of the more difficult tasks in the detection of changes is performing informed comparison when the datasets feature cluttered scenes and are acquired from different locations, where problems as occlusion and spatial sampling resolution become a major concern to overcome. While repeating the same pose parameters may be advisable, such demand cannot always be met, thus calling for a more general solution that can be efficient and be applied without imposing any additional constraints. In this paper, we propose a general detection strategy and analyze the actual effect of error sources and artifacts associated with laser scanning, particularly terrestrial based. The focus of this analysis is not only on the actual sources but on their interaction when two or more scans are compared. Finding an adequate representation and deriving an error aware model leads to an efficient and reliable scheme for detecting changes.