A lot of people who have heard of Terrasolid know about TerraScan’s large number of tools for working with airborne laser scanning (ALS) and mobile laser scanning (MLS) datasets. Did you know, however, that many of the algorithms found therein can be used for any point cloud data no matter their origin. To accommodate, Terrasolid has been steadily adding new tools over the years to simplify the process of getting point clouds from other data sources into the product, for correcting the spatial information into real world coordinates, as well as adding more algorithms to work with a wider variety of point cloud datasets.
While similar in some ways to airborne laser scanning, unmanned aerial LIDAR systems have some unique characteristics. The low-cost systems of choice for many are comprised of lower quality laser scanners aimed at the automotive market, such as Velodyne. The point clouds from these systems tend to have a noisy band of data from which one wishes to extract the ground to generate an elevation model. Running TerraScan’s hard surface algorithm originally introduced for MLS can accomplish this goal.
Additional tools help identity the ground in heavily vegetated areas where there are not as many returns. In addition, many of TerraScan’s above ground classification and feature extraction algorithms may be used with both UAV collected LIDAR and imagery. And since UAV datasets can be much smaller in size, UAV versions of each module exist to allow those working with small project sites access to these algorithms at a lower barrier of entry.
Point clouds that come from dense image matching (DIM) algorithms have characteristics not seen with ALS or MLS. Algorithms in TerraScan are aimed directly at Photogrammetric Point Cloud Processing. Taking the raw point cloud, removing the noise, finding the surfaces, and thinning the dataset to a manageable size allows for the traditional ALS algorithms to work nicely with these datasets to classify, extract features and run analysis on the point cloud.
In some areas, UAV systems are being used to collect imagery and derive a point cloud because the areas are too difficult to put or maintain control for temporal comparisons. TerraScan’s Fit to Reference can be used in such an instance to help match point clouds based on those areas that don’t change so that the detection and computation of volumetric changes can be carried out.
Point clouds generated from tripod mounted laser scanners, or terrestrial LIDAR systems see objects in a similar manner to MLS data, albeit with more shadowing. Importing the known location of the scanner, or deducing the scanner positions opens the ability to run some of the algorithms requiring knowledge of the scanner location for each point.
Knowing the scanner position allows for easily removing long range noise, classifying walls versus trees, or cutting the overlap between scan positions.
Handheld camera systems, such as the Matterport, generate point clouds using SLAM algorithms. The results are point clouds that are relative to a zero location. To incorporate them with BIM projects where one wishes to move from the data collected on the exterior to that from the interior it is necessary to put them in real world coordinates. This can easily be done using TerraScan’s Fit Using Targets to find control panels in the point cloud and derive a suitable transformation.
Similarly, handheld or backpack mounted laser scanners, such as Zeb Revo, use SLAM technology. TerraScan can automatically identify ball target control in the point cloud to transform not only the point cloud, but also the trajectory to real world coordinates. The trajectory can then be used with several algorithms in the products to colorize the point cloud from a co-mounted panoramic camera, or to clean, classify and extract features from the point cloud.
All these different types of datasets can be processed using the Terrasolid suite of tools.