Abstract
This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested.
Highlights
Nowadays, two groups of methods for shape recognition are commonly used in photogrammetry, i.e., passive methods based on digital image processing and active methods, which include, among others, Terrestrial Laser Scanning (TLS) [1]
The proposed automation of registration is a multi-stage process; it is based on the original software and it consists of (1) data conversion to the raster form, (2) aligning of pairs of raster TLS data for all possible combinations based on SURF and FAST algorithms, (3) the analysis of the quality of relative orientation of processed pairs, and (4) the final bundle adjustment
Scan eight was used as the reference scan and eight marked points were distributed over the test site, which were used for TLS data orientation
Summary
Two groups of methods for shape recognition are commonly used in photogrammetry, i.e., passive methods based on digital image processing and active methods, which include, among others, Terrestrial Laser Scanning (TLS) [1]. Each of these methods requires appropriate data pipeline processing to be performed, with the use of different algorithms that may be mutually complementary. These points are used in the Structure for Motion (SfM) approach, which allows for automatic determination of exterior orientation elements based on raster data [13,14,15,16,17]
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