The terrestrial Laser Scanner or LiDAR has seeing its applications on the fields of surveying engineering and geomatics increase as the evolution and improvements in its technology allowed the collection of increasingly more accurate data sets. Despite the benefits of LiDAR over other data collecting methods, such as remarkably higher amount of data collected for the same area in reduced periods of time without the need to place targets, the field of Deformation Monitoring has remained as one of the few unable to implement this technology. The reason behind this it is no other than the vast amounts of data LiDAR provides, this advantage becomes a weakness at the post-processing stage, where the analysis of large data sets acquired regularly presents a challenge.
OpsisOPSIS, is a software tool developed to overcome the impediment previously described. The idea within its core is to perform operations over the raw data from the LiDAR in order to obtain a significantly smaller data set to carry out the analysis. For the results obtained out of the analysis of this sub-dataset have to be equivalent as those we would obtain evaluating the original, the operations involved must ensure to preserve the spatial information contained within the input data.
LiDAR provides us with multidimensional spatial information in the form of point clouds. After going through a filtering process to eliminate the outliers or noise, the first calculation performed is the projection of each point of said cloud over a mathematically defined surface. Determination of the type of surface and its defining parameters it is done on the basis of best fit adjustment to the specific point cloud. From this moment on, we have the ability to display of our three-dimensional data on a two-dimensional representation, although the volume of data we have to work with remains the same, as we still gather the same number of points defined by 3 parameters (coordinates on the projection surface and distance). To resolve the obstacle presented by the extensive amount of data, the second transformation of the data is performed. Said transformation is the tessellation of the 2D representation using a squared grid of adjustable size. The points contained in each cell are the subject of a statistical analysis focused on the distance from each point to the surface, obtaining a mean deviation value for each grid’s cell. Setting a maximum deviation value, the cell are classified in two categories, accepted and rejected. The values of those accepted cells are the subject of the deformation analysis, thus achieving the goal of obtaining a reduced data set to analyze.
The task of monitoring the deformations or displacements of the scanned objects to perceive possible changes using this tool comes down to processing the subsequent point clouds obtained in different epochs using the same projection surface and grid obtained as the best fit for the initial point cloud. If significant changes in the object’s geometrical form were to occur, variations of the cell’s mean values will make them easily detectable. What is differentiating this approach from the commonly used existing software, is that this is not just a point cloud pair comparison. It is also a linear diagram visualization of the deformation history of each grid cell.
Such technology can be used for monitoring Dams, Bridges, Buildings, High Rise Towers, Tunnels, Chimneys, or other Structures, Landslides, Earth Surface settlements, Glaciers, Sand hills, etc.