| Abstract: | As a state-of-the-art mapping technology, mobile laser scanning (MLS) is increasingly applied to fields such as digital presentations of city environments. However, its application has recently met a bottleneck in data processing. It has been found that conventional methods for geometrically modeling 3D scattered points are inadequate when dealing with large volumes of MLS data. In fact, this is a challenge that has already been noted in the MLS-relevant fields, e.g. remote sensing, robot perception, and pattern recognition. A variety of algorithms under the schematic frame of analysis, modeling and synthesis (AMS) have been developed in these fields. The AMS paradigm is to first extract the implicit geometric primitives within each scan profile by geometrically modeling its 2D scattered points (GM2P). The resultant 2D geometric primitives are then integrated to restore the real 3D geometrical models. In this process, GM2P is a kernel procedure whereby a review of the GM2P algorithms is assumed to be of significance for developing new efficient algorithms for geometrically modeling 3D scattered points. This idea is supported by MLS sampling often being executed via parallel scan profiles. Indeed, the results of the literature review indicate an avenue for methodologically improving MLS in data processing. |
