costs of setting up and running a mobile mapping system have prevented us from bringing such systems into production at the
present time.
2.2.2 Terrestrial Scanning: In addition to the total stations
and GPS solutions traditionally provided by survey equipment companies, there are now many laser scanning and imaging
devices which may be used by topographic surveyors. Some of these are high-end and expensive, while others are simpler, less
accurate, but more portable and much cheaper. All of the systems we have tested are capable of producing 3D views of a scene,
either as a point cloud or a 3D interactive image. Survey equipment such as the Leica Scanstation P40 and the Trimble
TX8 laser scanners can capture one million points per second, at a positional accuracy of 2 or 3 mm. When capturing data over a
large area, such as a new commercial building, these scanners can rapidly produce extremely large datasets. For topographic
mapping purposes such equipment may be a little excessive, although mapping agencies could use them for detailed capture
of iconic buildings and on projects which require more detail and higher accuracy than traditional mapping data. Multistations,
such as the Leica Nova MS60, combine a total station with a laser scanning system to produce 3D point clouds augmented by
surveyed points from a single device. Figure 2 shows a 3D coloured point cloud of the OS Headquarters building, captured
using the MS60.
Developments in miniaturization have recently led to the production of systems that take the best of the mobile mapping
and terrestrial scanning worlds and combine them into a single- person mobile solution. Leica’s Pegasus:Backpack and Vexcel’s
Ultracam Panther use both cameras and laser scanners, mounted within a backpack to allow the surveyor to walk around a site,
capturing 3D data automatically in the process. Although we have only seen demonstrations of these systems, they appear to
provide a happy medium between the static terrestrial sensors and vehicle-mounted systems, with the added bonus that they can be
used to collect data indoors and in areas where a vehicle would have no access. Figure 3 shows a 3D point cloud of the OS
headquarters building in Southampton, Explorer House, captured using the Leica Pegasus backpack on a dull and very wet day.
The data were captured at a brisk walking pace, allowing a large volume of 3D data to be collected in a short period of time when
compared with a static terrestrial scanner.
A simpler solution to the capture of 3D point clouds in an indoor environment is provided by
Geoslam’s Zeb Revo system. This uses a hand-held laser scanner to capture X, Y, Z data as the
user walks around the target area. A cloud-processing system is then used to take the raw scans and produce a 3D point cloud.
The initial results of such a system are impressive – a quick walk
around a room can produce an accurate 3D point cloud within a few minutes of capture.
Figure 2. OS Explorer House mesh and point cloud from a Leica MS60 Multistation
As with any 3D point cloud capture system the question arises as to what to do with the points once they are collected and what
form of products to generate from them. Software does of course exist to view point clouds, but users are generally not familiar
with such software, nor the concept of a point cloud as a spatial entity. Users who are used to dealing with points, lines and
polygons in 2D will have to learn different methods of accessing and extracting meaningful information from point clouds. Point
clouds can of course be used to extract 2D data, e.g. computer aided design CAD drawings showing the position of walls,
doors etc. may be derived from indoor laser scanned data. Alternatively, the point clouds could be transformed into another
form, such as a 3D mesh or a 3D city model. The 3D mesh can look very good, but it is not easy to partition into real world
features. Attaching an attribute e.g. an address or a
topographical feature type such as “building” or “road” to a mesh is not straightforward, as the mesh may not be cleanly
partitioned between the features it represents. The 3D city model, such as a CityGML dataset, is easier to attribute, as the model
explicitly represents physical features such as buildings. An example of a 3D city model of the Explorer House building is
shown in Figure 4. Each of the different ways of representing 3D data has its advantages and disadvantages and different mapping
agencies may well take different approaches to the most appropriate model for their own circumstances.
Figure 3. OS Explorer House point cloud captured using a Leica Pegasus backpack
During the next few months, OS researchers will examine the datasets from the different terrestrial scanning technologies
discussed in this section, to determine which ones give us the data we require to update our existing products and to generate new
ones.
Figure 4. OS Explorer House 3D city model
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XLI-B4-727-2016
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3. AIRBORNE SURVEY
