Abstract
The current generation of Mobile Mapping Systems (MMSs) capture high density spatial data in a short time-frame. The quantity of data is difficult to predict as there is no concrete understanding of the point density that different scanner configurations and hardware settings will exhibit for objects at specific distances. Obtaining the required point density impacts survey time, processing time, data storage and is also the underlying limit of automated algorithms. This paper details a novel method for calculating point and profile information for terrestrial MMSs which are required for any point density calculation. Through application of algorithms utilising 3D surface normals and 2D geometric formulae, the theoretically optimal profile spacing and point spacing are calculated on targets. Both of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings-all important features in asset management surveys.
Highlights
Mobile Mapping Systems (MMSs) operating laser scanners are capable of producing high density point clouds, but this results in high data volumes and increased processing times
The distribution of points on an object influences the success of the algorithm, for example [8,9,10] require a minimum number of points per scan line to recognise a cylindrical object and the work presented by [11] demonstrated the importance of a high point density on spatial accuracy
Modern MMSs operate a 2D, full-circle laser scanner designed for mobile surveys. 2D scanners utilise the forward motion of the vehicle to provide 3D data (Figure 1b)
Summary
MMSs operating laser scanners are capable of producing high density point clouds, but this results in high data volumes and increased processing times. Interrogating this data is extremely time consuming and automated algorithms play an important role in processing. 2D scanners utilise the forward motion of the vehicle to provide 3D data (Figure 1b) When this scanning pattern intersects with a planar surface, the laser points are distributed over the surface in a linear pattern. StreetMapper [25], the point and profile algorithms described in this paper focus on 2D full circle scanners only Existing work in this area calculates point distribution through three main methods: manual measurements, geometric formulae and LiDAR simulations
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