Underwater photogrammetry 
HN 116 — 06/2020 
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resulting reduced point cloud of MBES and the 
photogrammetric point cloud were then analysed 
for the surface density. This measure calculates 
the amount of points In a given neighbourhood 
to estimated planes of a radius R and extrapolates 
this number to 1 m 2 R was chosen to 20 cm, I.e. 
approximately 2 x GSD of the MBES data. As the 
Investigated structure was mostly flat, this metric 
was chosen over the volumetric density. Tabje.3 
shows the number of points and densities next 
to the total area covered by the given data set. It 
Is worth mentioning that the covered area Is es 
timated by projecting the point clouds to a plane 
and then calculating the area of the enclosing 
polygon. Thus, the amount of points cannot be 
simply divided by the covered area to obtain the 
mean surface density. 
The table prominently shows the complemen 
tary characteristics of the two sensors. Where, on 
the one hand, the photogrammetric data had 
a high density with several hundred thousand 
points per sguare metre, the MBES provided a 
very much higher coverage of the area In a com 
parable amount of time needed for each of the 
methods. 
4 Summary and outlook 
The purpose of this feasibility study was to fuse 
MBES data with 3D point clouds derived from 
photogrammetric Image triangularon In order to 
Improve data density. The paper presents a pro 
cessing chain for combining hybrid point clouds 
generated from photogrammetric Imagery and 
hydroacoustic systems. MBES data hereby posed 
an Initial starting point thanks to Its good abso 
lute positioning accuracy and thus was used to 
georeference and scale all further data. The point 
cloud from Imagery was then to be transformed to 
best match the MBES data via ICP. 
The resulting data set provides an objective rep 
resentation of a wreck, suitable for Improved deci 
sion-making concerning underwater obstructions. 
It shows good congruence of MBES data with the 
photogrammetric point cloud. Even though, apart 
from the MBES data, no Independent scale could 
be Included for this data set, It Is observable that 
the deviations between the two data sets achieves 
an expected level of precision of 5.3 cm. Major 
deviations result mostly In areas that are hard to 
ensonlfy by the MBES (I.e. occluded areas near the 
seafloor) and on the edge of the photogrammetric 
point cloud with probably fewer measurement 
samples and a worse acgulsltlon geometry. The 
surface density Is highly Improved by several or 
ders of magnitude due to the Imagery-derived 
point cloud. 
For point cloud registration, Initial values were 
obtained by manually selecting salient points 
such as the bow In the Imagery. Determining 
correspondence between the salient points In 
I Data set 
Number of points 
Area covered [m 2 ] 
Surface density [pts/m 2 ] 1 
MBES full 
160,661 
498 
255 
MBES closest points 
3,791 
22 
158 
Photogrammetry 
12,324,258 
22 
535,069 
Table 3: Point density metrics ofthe photogrammetric point cloud and MBES data 
both point clouds was gulte challenging due to 
missing local geometric gradients on the wreck's 
starboard side. Automatic detection by suitable 
feature detectors or marked positions should sig 
nificantly Improve this processing step and avoid 
manual Interaction In future works. Furthermore, 
colour Information was Included by this fusion 
using Image enhancement as a preprocessing 
step. With this method, a green colour cast was 
removed and a more natural representation of 
the wreck was thus achieved. Though processing 
times of photogrammetric data may go up to sev 
eral hours or even days with large data sets, many 
processing steps can be automated to a certain 
degree. 
During the underwater Image acgulsltlon, sev 
eral practical problems occurred which had to be 
taken Into account when performing observations 
from a ROV and Integrating a camera system. The 
tether connected to the ROV was often tangling 
around edges of the wreck, thus limiting manoeu 
vrability. Furthermore, the cable of the camera was 
a regular Ethernet cable that had not been espe 
cially ruggedlsed for high sea applications and was 
rather short (70 m). This resulted In only parts of 
the starboard side of the wreck being observed. 
Future developments will lead to Improvements 
on these Issues. Since turbid water conditions re- 
gulred short acgulsltlon distances of approximate 
ly 1 m, the remote control was a challenging task 
as well. Currents, tides and tether entanglement 
often Interfered with the operating commands 
and thus affected the movement of the ROV. 
Therefore, practice and careful manoeuvring dur 
ing such challenging acgulsltlons are Imperative. 
The presented workflow Is not restricted to a 
ROV-borne data acgulsltlon. Continuous develop 
ments In sensor technology leads to miniaturised 
sensors that allow diver-based applications, too. In 
this context, the Integration of additional sensors 
on the measurement platform providing com 
plementa! Information (such as sonar and Inertial 
measurement unit) Is also part of our future work. 
It can be concluded that the photogrammetric 
method provides a higher grade of detail and ac 
curacy, even when observing geometrically chal 
lenging structures, such as a sidewall providing 
only very low geometric Information In line of 
sight, posing a dead reckoning problem. Using 
Image enhancement, significant contrast Increase 
could be achieved, resulting In more Images being 
aligned and higher coverage ofthe wreck. This ap-