CHANGE DETECTION VIA TERRESTRIAL LASER SCANNING

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 is an advisable strategy, such demand cannot always be met, thus calling for a more general solution that can be efficient and perform without imposing any additional constraints. In this paper, we propose a general detection strategy based on terrestrial laser scanning data. We analyze the different sources of complexity involved in the detection of changes and study their implication for terrestrial laser scans. Based on this analysis we propose a detection model, which is both aware of these hurdles and is efficient. We show that by finding an adequate representation of the data, efficient solutions can be derived. We then demonstrate the application of the model on several natural scenes and analyze the results.