This document discusses advances in surveying technology and algorithms used to analyze survey data. It explains that newer laser scanning and data collection methods produce more detailed terrain data but that older filtering algorithms cannot fully utilize this level of detail. The document then describes different point cloud filtering algorithms, noting that newer segmentation-based methods can better preserve terrain features and discontinuities. Test results show these newer algorithms generate more accurate 3D models of the underlying terrain compared to traditional smoothing algorithms.

1. By: Brian Mitchell
BACKGROUND
CONCLUSION
ABOUT THE ALGORITHM
Figure 1: Model of Airborne LiDAR
ABSTRACT
OLD VS. NEW METHODS
SURVEYING ENGINEERING TECHNOLOGY
POINT CLOUD FILTERING ALGORITHMS: OLD VS. NEW
The material that follows deals with the
technological advances that are
constantly occurring in the surveying
engineering field. It also explains how
the changing technology is causing a
change in how the analysis of a
surveyors recorded data. This report
provides a background on the process
that is used for collecting elevation
data, information about the algorithms
that are used to filter the point clouds
of data, which are then used to create
digital models of the terrain observed
by surveyor. It also explains the
differences between older and newer
algorithms, and describes why the
newer ones are needed in order to
keep up with the changing technology.
Technical images are used to
demonstrate, visually, what is
described and all of the information
and data comes from personal
knowledge of the topics or scholarly
articles.
An algorithm is a self contained step-by-step
set of operations that are performed in order
to make calculations. Process data, or
automate reasoning. In this process the
algorithms used to filter the point cloud data
sift throw the points and remove ones that are
erroneous, or don’t seem to fit. The unwanted
measurements are usually characterized by
one of three things, noise, outliers, or errors.
Once the algorithm determines which point do
not correlate with the actual terrain, the data
can then be analyzed again, with another
algorithm, but this time more focused.
There are several different algorithms that
deal with different types of terrains. Because
some terrains could be heavily wooded or
consist of buildings, specific algorithms either
record the elevations of the top of the objects
or just remove them all together. This can be
seen in Figure 2, were the buildings have
been removed.
In this report the differences between traditional and newer filtering
algorithms for point cloud analysis is presented. As my research
has shown, newer algorithms are more accurate than the older
ones, making it more important to use the newer ones. By using
these types of algorithms as opposed to the older ones, maps and
three dimensional models that are produced from the data, will be
able to show more, like abrupt terrain features and dramatic
elevation changes. Because of this, users will be able understand
the terrain more accurately before actually seeing it.
In the field of surveying, advances in technology, that
have occurred over time, have had significant impacts
on the daily tasks of a surveyor. In the past, it was
more difficult to analyze and record the elevations and
patterns of earths terrain, but in todays practice of land
surveying there are easier and more technical
methods of obtaining this information.
Technological advances have had a significant in the
process of map making. With the use of computer
software programs, such as AutoCAD and Carlson, the
work of a surveyor is becoming more digital. Because
these programs allow one to easily draw and save
projects while working from a basic computer screen
and not hand drawn like they were previously done,
maps are now able to take to new shapes.
While traditional maps are only two dimensional,
newer technology is allowing terrain to be viewed and
recorded in three dimensions. Technology like LiDAR,
or terrestrial laser scanning, has played a huge role in
making this possible.
LiDAR stands for light detection and ranging, but is
more commonly referred to as light radar. This
technology is used in surveying to measure the
distances between the point that the light is emitted, on
the instrument, and another point. LiDAR can be used
while either stationary or in motion. Because it has the
ability to record data while moving, surveyors began
affixing these laser scanners to the under carriage of
airplanes, as seen in Figure 1. As the airplane travels
in its flight direction, the laser sweeps across the
terrain, scanning it to measure the distance between
the plane and the ground. The plane’s altitude is
monitored from the ground by a reference station, and
the elevation of the terrain is calculated from the
altitude and the LiDAR data.
From this information, digital elevation models can be
created. The data is collected from using the LiDAR
technology is stored as a point cloud, which is simply a
set of data points in a specific coordinate system.
Once there is a set of recorded points, the cloud is
then filtered using one or more algorithms to analyze
the data, and eventually used to create the three
dimensional digital elevation model.
[1] Axelsson, P. (1999). “Processing of laser
scanner data – algorithms and
applications.” Photogrammetry and
Remote Sensing, 54, 138-147.
[2] Hui, Z., Hu, Y., Yevenyo, Y. Z., Yu, X.
(2016). “An Improved Morphological
Algorithm for Filtering Airborne LiDAR Point
Cloud Based on Multi-Level Kriging
Interpolation.” Remote Sensing, 8(35), 1-
16.
[3] Sithole, G. and Vosselman, G. (2004).
“Experimental comparison of filter
algorithms for bare-Earth extraction from
airborne laser scanning point clouds.”
Photogrammetry and Remote Sensing, 59,
89-91.
[4] Sithole, G. and Vosselman, G. (2012).
“Filtering of Airborne Laser Scanner Data
Based on Segmented Point Clouds.”
Research Gate, 66-71.
[5] Yong, L., Bin, Y., Huayi, W., Ru, A. and
Hanwei, X. (2014). “An Improved Top-Hat
Filter with Sloped Brim for Extracting
Ground Points from Airborne Point
Clouds.” Remote Sensing, 6, 12885-12095.
While the technology used in the field of surveying has rapidly
been evolving over time, the methods that are used to analyze the
data gathered by these new technologies has been changing as
well. Because the older/current algorithms are becoming outdated
due to the technology being more precise, the algorithms that are
used to filter the point clouds, are becoming more advanced.
It is said that, “current algorithms for filtering point clouds assume
the Earth’s surface to be continuous in all directions” (Sitholea and
Vosselman, 2012). With this being said the digital elevation models
that are produced using these types of algorithms are not very
accurate. While they are able to show the general terrain, “most
traditional morphology-based algorithms have difficulties in
preserving abrupt terrain features” (Hui et al. 2016).
Because the algorithms that have been highly used in the past,
and are still somewhat used now are proving to be very inaccurate,
newer types have been developed based on many different ideas,
including segmentation. Segmentation breaks up the point cloud
into segments that still might have height discontinuities. This
effectively smooths out the point cloud by removing small objects,
that are irrelevant, in order to see the bigger ones more clearly.
This in turn allows for the terrain models to become more accurate,
due to the discontinuities being seen more noticeably, as shown in
Figure 4. This figure shows that the new algorithm is the most
accurate out of the ones that were tested along with it. Figure 3
also represents the errors that each algorithm makes in
comparison to one another. Once again the new algorithm is the
most accurate.
REFERENCES
Figure 2: Three views of same terrain before and after filtering.
Figure 3: Comparison of new algorithm
and three others.
Figure 4: Models of the compared
algorithms from Figure 3.