Entering the Point Cloud - RIEGL Laser Measurement Systems

Entering the
Point Cloud
Data analysis
takes center stage
in part two of our
mobile mapping
road trip.
By Joshua I. France, Riegl USA (www.
rieglusa.com), Orlando, Fla., and Craig
Glennie, University of Houston (www.
uh.edu), Houston, Texas.
As detailed in the last issue of Earth
Imaging Journal, a recent mobile
mapping project was motivated
by a highway paving company’s
need for better data so it could
manage its costs and bidding approach
more effectively. Upon completing data
collection for the project along a ninemile,
two-lane section of Florida’s Interstate
95, we were confident the processed
data would provide complete road surface
information for the site.
Now our journey on the waveform
continues, as we venture into the software
portion of the mobile mapping system
and into the point cloud itself. The
trip begins with data transfer, a step that
separates the acquisition computer from
the processing functions and software.
Such separation allows for better data
management and for the acquisition staff
to collect the next project.
The on-ramp into the point cloud led to a nine-mile,
two-lane section of Florida’s Interstate 95.
System Specifics
Processing the light detection and
ranging (LiDAR) data with the project’s
mobile scanning system, the Riegl VMX-
250, differs from processing airborne
LiDAR data or other systems’ LiDAR
data. The VMX-250’s two VQ-250 laser
scanners use an advanced signal processing
technology called online waveform
processing. The technology enables full
waveform analysis and rigorous multitarget
detection to occur during data
acquisition. A single Riegl VQ-250 can
process 300,000 laser shots with multitarget
capability in terms of range and
amplitude measurements in one second.
Online processing allows for the data to be
quickly georeferenced—in Riegl’s RiProcess
software suite—by simply unpacking the
data from a compressed file format and
assigning real-world coordinates based on
the selected coordinate system.
It took 1 hour and 12 minutes to collect
18 miles of data on a single Quad-
Core computer with a solid-state drive
running eight tasks in parallel for processing
to a raw point cloud. This workflow
included decompression from the raw
data format and then georeferencing and
projecting the data into the desired coordinate
system, processing the trajectory
separately using Applanix’s POSPac Mobile
Mapping Suite. The 18-mile stretch
of highway provided excellent Global
Navigation Satellite System conditions,
with only a few interruptions to satellite
lock. This made for a quick, easy trajectory
processing experience. Once the data
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were processed, we assessed the data’s
quality—first to themselves and second
to the survey control points. These quality
control checks happen before the data
leave the RiProcess software suite.
Data Analysis
The analysis process entails visually
inspecting the data by first examining the
system’s calibration. Because the Riegl
VMX-250 is calibrated by the factory
and the scanners are fixed in place on
the measurement unit, the alignment of
the scanners doesn’t drift, but it should
be checked as part of any quality control
process. Figure 1 gives a vertical difference
between the data from the two
scanners in a single pass and shows that
the vertical alignment of scanner one
and two are within 0.02 feet, which is
indicated by the blue-teal color. An area’s
cross section, shown in Figure 2, also can
provide additional analysis of the alignment’s
quality.
An analysis of the highlighted (yellow)
section of the bridge deck, shown
at the left side of Figure 2, includes
3,260 points—an area 2.5 feet by 8 feet—
and has a planar standard deviation of
0.016932 feet in the vertical direction.
The barrier wall of the bridge provided
a visual confirmation of the horizontal
Figure 1. A height difference plot compares single-pass scanner 1 and scanner 2.
Figure 2. A cross section of a bridge was used to examine scanner alignment and calibration.
The analysis process
entails visually
inspecting the data by
first examining the
system’s calibration.
alignment. With 3,556 points, the right
side of Figure 2 shows a standard deviation
of 0.025065 feet. This quick analysis
gives us confidence that the relative
alignment of the scanners is stable and
no further adjustment needs to be performed.
This brings us to examining the
relative accuracy of two separate passes.
Dual-Pass Analysis
Again, we use a visual assessment to
examine the height differences between
the two passes. Because of the accuracy
of the Global Positioning System (GPS),
the height offset between two passes can
drift from 0 to 4 inches of separation. As
shown in Figure 3, the relative accuracy
drifts to a 1.2-inch gap between the two
passes. The LiDAR control target shows
up clearly in the data as seen from the
top in reflectance view, Figure 4, where
© 2011 Earthwide Communications LLC, www.eijournal.com
Figure 3. Raw data separation was attributed to a relative difference in GPS positioning.
Figure 4. Here the raw data match the control point.
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All changes are
reviewed and
adjusted at control
points as needed,
and then the data are
reprocessed to apply
the changes.
Figure 5. The tool interface allows users to define the size and shape of a cross section to repeat throughout the data.
Figure 6. The Scan Alignment tool quality table shows
areas that need more attention.
the control chevron is seen in red. Reflectance
is a calibrated intensity product
of the system that allows for materials to
have the same value whether 5 feet or 50
feet away from the scanner. Now we need
to resolve the GPS-induced variations in
the data to deliver a consistent surface.
Closing the Gap
At this point in the workflow processing,
it’s clear that the two scanners on the
Riegl VMX-250 are well aligned; however,
the multiple passes need adjustment to
bring parts of the mission path into a
closer alignment—probably due to limitations
in the accuracy of the differential
GPS solution. The goal is to reduce any
vertical offset between passes so the data
are merged into a single cloud. Although
the vertical relative alignment of multiple
paths are within the GPS accuracy
estimate of 2 inches, semi-automated
extraction programs have a hard time
determining where the ground lies when
the point cloud is separated by even these
small vertical differences.
Riegl Laser Measurement Systems has
developed scan alignment tools to close
relative data gaps based on observations
in the LiDAR data and trajectory quality
information. Such tools allow users
to quickly analyze and correct data. By
delivering high-quality data to third-party
software packages for feature extraction,
the whole process from collection to data
delivery is smoother, more efficient and
able to take full advantage of automated
classification, segmentation and object
extraction routines.
The first step in scan alignment is
to make cross sections for each control
point section by making a single section
and cloning it over the distance between
control points, as shown in Figure 5.
Some adjustment is required to trim out
areas along the road that aren’t of primary
concern, such as underpasses and vegetation.
Once complete, an initial relative
alignment calculation can be run for each
control point site. Then each area is assigned
a quality indicator, and areas with
a yellow or red indicator require more
attention, as shown in Figure 6.
Each area can be selected for refinement
based on a couple of parameters and
filter options, and an absolute alignment
Figures 7 and 8. Changes before (left) and after (right) alignment can be compared in real time.
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can be performed at each control
point. Shifts can be made to the
height and across positions for
alignment. Typically the height
is adjusted first, and an across
shift is applied during refinement.
The maximum plane separation
distance, plane definition point
variation and search radius can
be adjusted to fit the user’s needs.
The changes before (Figure 7) and
after (Figure 8) alignment can be
compared in real time to help
the user visualize how the point
cloud has been adjusted.
All changes are reviewed and
adjusted at control points as needed,
and then the data are reprocessed
to apply the changes. The
resulting point cloud now can be
inspected between control points
for any misalignment pockets.
Using the scan alignment tool,
quick work is made of closing the
gap to mitigate trajectory-induced
errors in terms of weighted averaging
of statistically independent
measurements, as well as adjusting
the data to the accuracy of the
absolute control. Some additional
work to smooth out other parts of
the data between control points
may also be done the same way.
Now the resulting point cloud is
within the combined noise level
of the laser sensors and independent
ground control. In typical
surveys, the point cloud shows
vertical deviations of less than a
half inch, as seen in Figure 9.
End of the Processing Highway
The road of quality control
analysis comes to an end just
before the many off-ramps leading
to feature extraction software.
The final few feet of the road are
for data export, which is done
with ease thanks to the simple
export user interface in RiProcess.
In most cases the user simply
selects a file location and stores
the point cloud in the ubiquitous
LAS LiDAR data file format
(www.liblas.org). In the next
issue of Earth Imaging Journal,
we’ll arrive at our destination
with a look at some of the feature
extraction tools and uses of the
Riegl VMX-250 mobile data.
Figure 9. A section of the dual-pass adjusted highway displays vertical
deviations of less than one-half inch.
© 2011 Earthwide Communications LLC, www.eijournal.com
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